This server provides comprehensive supply chain security intelligence, unifying 21 data sources into a single MCP interface for AI agents to perform full-spectrum package and vulnerability analysis.
Vulnerability & Threat Intelligence
Query OSV, GHSA, NVD, and Go Vulnerability Database for known CVEs by package, commit hash, or PURL
Get EPSS exploit prediction scores (single, batch, or threshold-filtered) and check CISA Known Exploited Vulnerabilities (KEV) catalog
Package Registry Metadata
Fetch metadata, version histories, maintainers, download stats, and lifecycle scripts across 7 ecosystems: npm, PyPI, crates.io, RubyGems, NuGet, Packagist, and Go module proxy
npm extras: provenance attestations, bulk audit, and download anomaly detection
Dependency Analysis
Explore dependency trees, reverse dependencies, similar packages, and version requirements via deps.dev and Libraries.io (including SourceRank quality scores)
Provenance & Integrity
Verify npm Sigstore provenance, search/verify Rekor transparency log entries by email, hash, or public key, and validate Go module checksums
Security Posture & Quality
Assess OpenSSF Scorecard scores (with side-by-side repo comparison), Best Practices badge status, and Repology distribution packaging issues
Typosquatting Detection
Detect suspicious package names using Levenshtein distance (≤ 2) against popular packages and warn about confusable characters
License Compliance
Look up curated license data from ClearlyDefined for individual packages or in batch
Aggregated Workflows
Composite tools (vuln_scan, package_info, dep_tree, security_check, quality, license) run multiple sources in parallel for unified risk assessment
List all 21 data sources with configuration status and required API keys
Provides tools for querying GitHub Security Advisories (GHSA) and leveraging GitHub API for enhanced rate limits on vulnerability and security data.
Provides tools for querying Libraries.io to obtain dependency metrics, source rank, and library intelligence across multiple ecosystems.
Provides tools for querying npm package metadata, including package details, provenance verification, and security advisory information.
Provides tools for querying NuGet package metadata and associated vulnerability data.
Provides tools for querying Packagist package metadata and security information.
Provides tools for querying PyPI package metadata and vulnerability information.
Provides tools for querying RubyGems package metadata and security advisories.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@supply-chain-mcp-server audit package express 4.18.2 for vulnerabilities"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Supply chain security intelligence is the missing layer in modern software development. Vulnerability databases, package registries, provenance verification, exploit prediction scoring, license compliance, security scorecards, typosquatting detection — the data you need is scattered across dozens of platforms, each with its own API, its own auth, its own rate limits, its own output format. Today you check OSV in one tab, NVD in another, pull up npm advisories, check EPSS scores on FIRST.org, verify Sigstore provenance in yet another tab, look up OpenSSF Scorecard, cross-reference CISA KEV, and then spend an hour manually piecing it all together.
Traditional supply chain security workflow:
check vulnerabilities -> OSV + NVD + GitHub Advisories (3 separate UIs)
assess exploit likelihood -> EPSS scores (separate API)
check active exploitation -> CISA KEV catalog (separate JSON feed)
verify package provenance -> Sigstore/Rekor + npm attestations (multiple CLIs)
check security practices -> OpenSSF Scorecard + Best Practices Badge (2 UIs)
audit dependencies -> deps.dev + Libraries.io (2 more UIs)
check registry metadata -> npm + PyPI + crates.io + Go (4 registries)
check for typosquatting -> manual name comparison (error-prone)
verify license compliance -> ClearlyDefined (another UI)
check distro packaging -> Repology (yet another UI)
────────────────────────────────
Total: 45+ minutes per package audit, most of it switching contextssupply-chain-mcp-server gives your AI agent 7 composite tools (orchestrating 90 techniques internally) across 21 data sources via the Model Context Protocol. The agent calls a single tool like vuln_scan or package_info, which automatically fans out to the right techniques in parallel, correlates vulnerability data with exploit predictions, verifies provenance, checks security posture, and presents a unified supply chain risk assessment — in a single conversation.
With supply-chain-mcp-server:
You: "Audit the security posture of the express npm package"
Agent: -> vuln_scan(mode:"package", ecosystem:"npm", name:"express")
internally runs: osv_query + ghsa_package + epss_batch + kev_lookup
-> package_info(ecosystem:"npm", name:"express", version:"4.21.2")
internally runs: npm_package + npm_downloads + npm_maintainers + npm_scripts + npm_provenance
-> quality(mode:"scorecard", owner:"expressjs", repo:"express")
internally runs: scorecard_repo
-> "express has a strong security posture. 2 known vulns but
neither actively exploited (not in KEV). High EPSS on one
CVE warrants patching priority. Provenance verified via
Sigstore. OpenSSF Scorecard 8.2/10."Related MCP server: DepScope
Existing tools give you raw data one source at a time. supply-chain-mcp-server gives your AI agent the ability to reason across vulnerability, provenance, and package intelligence simultaneously.
npx supply-chain-mcp-server7 composite tools (90 techniques) work immediately. No API keys required for most data sources — 18 of 21 sources are fully free.
git clone https://github.com/badchars/supply-chain-mcp-server.git
cd supply-chain-mcp-server
bun installexport GITHUB_TOKEN=your-token # GHSA + Scorecard higher rate limits
export LIBRARIES_API_KEY=your-key # Required for Libraries.io tools
export NVD_API_KEY=your-key # 50 req/30s vs 5 req/30s without keyAll API keys are optional. Without them, you still get 6 of 7 composite tools fully operational — only Libraries.io (within dep_tree) requires an API key.
# With npx
claude mcp add supply-chain-mcp-server -- npx supply-chain-mcp-server
# With local clone
claude mcp add supply-chain-mcp-server -- bun run /path/to/supply-chain-mcp-server/src/index.tsAdd to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"supply-chain": {
"command": "npx",
"args": ["-y", "supply-chain-mcp-server"],
"env": {
"GITHUB_TOKEN": "optional",
"LIBRARIES_API_KEY": "optional",
"NVD_API_KEY": "optional"
}
}
}
}Same JSON config format. Point the command to npx supply-chain-mcp-server or your local installation path.
You: "Check if express@4.17.1 has any known vulnerabilities and verify its provenance"That's it. The agent handles vulnerability lookups, EPSS scoring, KEV checks, provenance verification, and more automatically.
You: "Do a complete security audit of the lodash npm package"
Agent: -> vuln_scan {mode: "package", ecosystem: "npm", name: "lodash"}
↳ osv_query + ghsa_package + epss_batch + kev_lookup (parallel)
-> package_info {ecosystem: "npm", name: "lodash", version: "4.17.21"}
↳ npm_package + npm_downloads + npm_maintainers + npm_scripts + npm_provenance + npm_attestations + npm_audit_bulk (parallel)
-> quality {mode: "scorecard", owner: "lodash", repo: "lodash"}
↳ scorecard_repo
-> license {mode: "lookup", type: "npm", name: "lodash", version: "4.17.21"}
↳ license_lookup
-> "lodash@4.17.21 security audit:
Vulnerabilities: 3 historical (all fixed in 4.17.21),
including prototype pollution CVE-2020-8203.
Provenance: No Sigstore attestation (published pre-provenance era).
OpenSSF Scorecard: 6.1/10 — no branch protection, no fuzzing.
Dependencies: Zero runtime deps (self-contained).
License: MIT (ClearlyDefined confirmed).
Lifecycle scripts: None — clean.
Recommendation: Safe to use at latest version. Consider
migrating to lodash-es for tree-shaking."You: "Triage CVE-2024-3094 — how urgent is this?"
Agent: -> vuln_scan {mode: "cve", cve_id: "CVE-2024-3094"}
↳ nvd_cve + osv_id + ghsa_id + epss_score + kev_lookup (parallel)
-> "CVE-2024-3094 (xz-utils backdoor):
CVSS: 10.0 Critical — backdoor in xz-utils 5.6.0/5.6.1
EPSS: 0.95 (99th percentile) — extremely likely to be exploited
CISA KEV: YES — actively exploited, deadline March 2024
Affects: xz-utils/liblzma on Linux (sshd via systemd)
This is CRITICAL. Immediate action required:
1. Downgrade xz-utils to 5.4.x or remove 5.6.0/5.6.1
2. Check all systems for affected versions
3. This was a sophisticated supply chain attack via
a compromised maintainer."You: "Assess the supply chain risk of adding fastapi to our Python project"
Agent: -> package_info {ecosystem: "pypi", name: "fastapi"}
↳ pypi_package + pypi_releases + pypi_maintainers (parallel)
-> vuln_scan {mode: "package", ecosystem: "PyPI", name: "fastapi"}
↳ osv_query + ghsa_package + epss + kev (parallel)
-> dep_tree {mode: "dependencies", ecosystem: "pypi", name: "fastapi", version: "0.115.0"}
↳ deps_dependencies + deps_requirements + libraries_deps (parallel)
-> quality {mode: "full", owner: "tiangolo", repo: "fastapi", repo_url: "https://github.com/tiangolo/fastapi"}
↳ scorecard_repo + badge_by_repo (parallel)
-> security_check {mode: "typosquat", name: "fastapi", ecosystem: "pypi"}
↳ typosquat_check
-> "fastapi supply chain risk assessment:
Vulnerabilities: 1 low-severity (fixed in latest).
Dependencies: 5 direct (starlette, pydantic, etc.), 23 transitive.
OpenSSF Scorecard: 7.4/10 — good CI/CD, signed releases.
Maintainer: single primary maintainer (tiangolo).
Typosquatting: 'fast-api' and 'fastapi2' exist — verify
you're installing the correct package.
Risk: LOW. Well-maintained, active community, good security
practices. Single-maintainer risk is mitigated by strong
automation and community oversight."You: "I'm about to install 'reqeusts' from PyPI — is this safe?"
Agent: -> security_check {mode: "typosquat", name: "reqeusts", ecosystem: "pypi"}
↳ typosquat_check
-> security_check {mode: "compare_names", name_a: "reqeusts", name_b: "requests"}
↳ typosquat_compare
-> "WARNING: 'reqeusts' is a likely typosquat of 'requests'.
Edit distance: 2 (transposed 'ue' -> 'eu')
Similarity: 87.5%
The legitimate package is 'requests' (800M+ monthly downloads).
'reqeusts' has very low downloads and was likely published
to exploit typos. DO NOT install this package.
Correct command: pip install requests"The AI agent sees only 7 composite tools (~1K tokens), each orchestrating multiple techniques in parallel internally.
Composite Tool | Techniques | Modes | Description |
| 24 |
| Vulnerability intelligence across OSV, GHSA, NVD, EPSS, CISA KEV, Go Vuln DB |
| 33 | by ecosystem | Package metadata — auto-dispatches to npm (10), PyPI (4), crates.io (4), Go (3), RubyGems (4), NuGet (4), Packagist (4) |
| 14 |
| Dependency graph intelligence via deps.dev + Libraries.io |
| 7 |
| Supply chain threat detection: typosquatting, provenance, Sigstore |
| 8 |
| Project quality: OpenSSF Scorecard, Best Practices Badge, Repology |
| 3 |
| License compliance analysis via ClearlyDefined |
| 1 | — | List all data sources with configuration status |
Run npx supply-chain-mcp-server --list-all to see the full list.
Tool | Description |
| Query OSV database for known vulnerabilities affecting a specific package and optional version |
| Query OSV database for vulnerabilities associated with a specific git commit hash |
| Query OSV database using a Package URL (purl) for known vulnerabilities |
| Batch query OSV database for vulnerabilities across multiple packages at once |
| Fetch full vulnerability details from OSV by ID (OSV, CVE, GHSA, RUSTSEC, PYSEC, etc.) |
Tool | Description |
| Fetch a GitHub Security Advisory by its GHSA or CVE identifier |
| Search GitHub Security Advisories by keyword, ecosystem, and severity |
| List GitHub Security Advisories affecting a specific package in a given ecosystem |
| List the most recently updated GitHub Security Advisories |
Tool | Description |
| Fetch full CVE details from NVD (NIST National Vulnerability Database) by CVE ID |
| Search NVD for CVEs by keyword and optional CVSS v3 severity |
| Fetch recently published CVEs from NVD within a given number of days |
Tool | Description |
| Get EPSS exploit probability and percentile for a single CVE ID |
| Batch EPSS scores for multiple CVEs (up to 100) in a single request |
| Get the highest EPSS-scoring CVEs (most likely to be exploited) |
| Find CVEs with EPSS score above a given threshold |
Tool | Description |
| Check if a CVE is in the CISA Known Exploited Vulnerabilities (KEV) catalog |
| Search KEV entries by keyword (matched against vendor, product, name, description) |
| Get recently added KEV entries within the last N days |
| Get KEV catalog statistics: total count, top vendors, entries per year, and ransomware usage breakdown |
Tool | Description |
| Fetch npm package metadata including description, latest version, maintainers, license, repository, homepage, and publish timeline |
| Fetch metadata for a specific npm package version including dependencies, dist info (tarball, shasum, integrity), scripts, and deprecation status |
| Fetch npm download counts for a package over a given period (last-day, last-week, last-month) |
| Search the npm registry for packages matching a query string |
| Extract maintainers and publish timeline from an npm package — critical for detecting maintainer takeover attacks |
| Extract and analyze lifecycle scripts from a specific npm package version — flags suspicious commands (curl, wget, eval, exec, etc.) commonly used in supply-chain attacks |
| Check whether an npm package version has Sigstore provenance attestations and signatures |
| Bulk query npm security advisories for a set of packages and versions |
| Fetch full Sigstore attestation bundles for an npm package version — returns SLSA provenance and publish attestations |
| Get day-by-day npm download counts for a date range — useful for detecting download anomalies or dependency confusion attacks |
Tool | Description |
| Fetch PyPI package metadata including author, license, summary, project URLs, classifiers, and Python version requirements |
| Fetch metadata for a specific PyPI package version including release URLs with upload times, file sizes, digests, and yanked status |
| List all releases of a PyPI package with upload dates, sizes, and yanked status — useful for detecting suspicious rapid version bumps |
| Extract author and maintainer information from a PyPI package — useful for detecting ownership changes |
Tool | Description |
| Fetch crates.io crate metadata including description, download counts, max version, repository, homepage, categories, and keywords |
| List all versions of a crate with version number, yanked status, license, crate size, creation date, and download count |
| Fetch dependencies for a specific crate version including dependency kind (normal/dev/build), version requirement, and optional flag |
| List owners of a crate on crates.io — useful for detecting ownership changes |
Tool | Description |
| Fetch RubyGems gem metadata including name, version, authors, description, download counts, project URI, and source code URI |
| List all versions of a RubyGems gem with release dates, platform info, and version numbers |
| Search the RubyGems registry for gems matching a query string |
| Get reverse dependencies of a RubyGems gem — useful for assessing blast radius of a compromised package |
Tool | Description |
| Fetch NuGet package registration metadata including all versions, dependency groups, descriptions, and catalog entries |
| Search the NuGet registry for packages matching a query string |
| List all published versions of a NuGet package from the flat container index |
| Get specific version details from NuGet including dependency groups, description, license, and catalog metadata |
Tool | Description |
| Get PHP/Composer package metadata from Packagist including versions, description, maintainers, and repository information |
| Search Packagist for PHP packages matching a query string |
| Get Packagist package download statistics including total, monthly, and daily download counts |
| Get security advisories for PHP packages from Packagist — returns known vulnerabilities and CVEs |
Tool | Description |
| Fetch Go module info from the module proxy: latest version and all available versions |
| Fetch info and go.mod contents for a specific Go module version — returns parsed dependency list |
| Look up a Go module version in the checksum database (sum.golang.org) for hash verification |
Tool | Description |
| Fetch a Go vulnerability by its ID from the Go Vulnerability Database |
| List all Go vulnerability IDs from the database index |
| Get Go Vulnerability Database metadata including last modified time |
| Find Go vulnerabilities affecting a specific module |
Tool | Description |
| Look up a package on deps.dev to get metadata, versions, and security information |
| Get detailed info about a specific package version from deps.dev including links, licenses, and advisories |
| Get the dependency tree for a specific package version from deps.dev |
| Get packages that depend on a specific package version from deps.dev |
| Fetch a security advisory by key (e.g. GHSA-xxxx-xxxx-xxxx) from deps.dev |
| Get project information from deps.dev by repository URL |
| Look up a package by its SHA256 artifact hash on deps.dev |
| Get the requirements (version constraints) for a specific package version from deps.dev |
| Find similarly named packages on deps.dev for typosquatting detection |
| Look up a package by Package URL (purl) on deps.dev |
Tool | Description |
| Get the OpenSSF Scorecard security score for a GitHub repository, including individual check results |
| Compare OpenSSF Scorecard security scores across 2-5 GitHub repositories side by side |
Tool | Description |
| Get OpenSSF Best Practices badge status and criteria for a project by ID |
| Search OpenSSF Best Practices badge projects |
| Find OpenSSF Best Practices badge by GitHub repository URL |
Tool | Description |
| Get package metadata from Libraries.io including repository info, versions, and popularity metrics |
| Get dependencies for a specific package version from Libraries.io |
| Get packages that depend on a specific package from Libraries.io |
| Get the SourceRank quality score breakdown for a package from Libraries.io |
Tool | Description |
| Get curated license data for a software component from ClearlyDefined |
| Batch license lookup for multiple components via ClearlyDefined |
| Search ClearlyDefined for components by pattern |
Tool | Description |
| Search the Rekor transparency log by email, SHA256 hash, or public key fingerprint |
| Retrieve a specific Rekor transparency log entry by UUID, including body, attestation, and inclusion proof |
| Get the current Rekor transparency log status including rootHash, treeSize, and signedTreeHead |
| Retrieve multiple Rekor log entries by their UUIDs or log indexes in a single request |
| Verify whether a SHA256 artifact hash has been recorded in the Rekor transparency log |
Tool | Description |
| Get package versions across all Linux distributions from Repology |
| Find packaging problems/issues for a repository on Repology |
| Search Repology projects by name |
Tool | Description |
| Check if a package name is suspiciously similar to popular packages (potential typosquatting) — returns matches with edit distance <= 2 |
| Compare two package names directly to assess typosquatting risk, showing edit distance, similarity percentage, character-level diff, and confusable character warnings |
Tool | Description |
| List all 21 supply chain security data sources with configuration status, API key status, and tool counts |
# List 7 composite tools
npx supply-chain-mcp-server --list
# List all 90 individual techniques
npx supply-chain-mcp-server --list-all
# Run composite tools
npx supply-chain-mcp-server --tool vuln_scan '{"mode":"cve","cve_id":"CVE-2024-3094"}'
npx supply-chain-mcp-server --tool vuln_scan '{"mode":"package","ecosystem":"npm","name":"express"}'
npx supply-chain-mcp-server --tool package_info '{"ecosystem":"npm","name":"express","version":"4.21.2"}'
npx supply-chain-mcp-server --tool security_check '{"mode":"typosquat","name":"reqeusts","ecosystem":"pypi"}'
npx supply-chain-mcp-server --tool quality '{"mode":"scorecard","owner":"expressjs","repo":"express"}'
npx supply-chain-mcp-server --tool dep_tree '{"mode":"dependencies","ecosystem":"npm","name":"express","version":"4.21.2"}'
# Individual techniques also work (backward compatible)
npx supply-chain-mcp-server --tool osv_query '{"ecosystem":"npm","name":"express"}'
npx supply-chain-mcp-server --tool epss_score '{"cve_id":"CVE-2024-3094"}'Source | Auth | Rate Limit | What it provides |
None | 1 req/s | Cross-ecosystem vulnerability database (npm, PyPI, Go, Rust, etc.) | |
Optional | 1 req/s | Security advisories for GitHub-tracked packages | |
Optional | 5 req/30s (50 with key) | CVE details, CVSS scores, CPE matching, keyword search | |
None | 2 req/s | Exploit Prediction Scoring System — probability of CVE exploitation | |
None | 0.5 req/s | Known Exploited Vulnerabilities catalog with remediation deadlines | |
None | 2 req/s | Package metadata, versions, scripts, provenance, attestations, advisories | |
None | 2 req/s | Python package metadata, versions, releases, maintainer info | |
None | 1 req/s | Rust crate metadata, versions, dependencies, owners | |
None | 1 req/s | Ruby gem metadata, versions, reverse dependencies | |
None | 2 req/s | .NET package metadata, versions, dependency groups | |
None | 1 req/s | PHP/Composer packages, download stats, security advisories | |
None | 2 req/s | Go module versions, go.mod contents, checksum verification | |
None | 2 req/s | Go-specific vulnerability advisories by module | |
None | 5 req/s | Cross-ecosystem dependency graphs, advisories, project info, purl lookup | |
Optional | 1 req/s | Repository security scoring across 18 checks | |
None | 1 req/s | Best Practices badge status and criteria | |
| 1 req/s | Package popularity, SourceRank, cross-platform dependency data | |
None | 2 req/s | Curated license data across ecosystems | |
None | 2 req/s | Transparency log for software signing — provenance verification | |
None | 1 req/s | Package versions across Linux distributions | |
None | N/A | Built-in Levenshtein distance + confusable character analysis |
src/
index.ts # CLI entrypoint (--help, --list, --list-all, --tool, stdio server)
composite/ # 7 composite tools (MCP-registered)
helpers.ts # callTool(), callToolsParallel(), buildCompositeResponse(), TOOL_REGISTRY
vuln-scan.ts # vuln_scan — 24 techniques (5 modes)
package-info.ts # package_info — 33 techniques (7 ecosystems)
dep-tree.ts # dep_tree — 14 techniques (6 modes)
security-check.ts # security_check — 7 techniques (5 modes)
quality.ts # quality — 8 techniques (5 modes)
license.ts # license — 3 techniques (3 modes)
meta.ts # meta — 1 technique
index.ts # compositeTools barrel export
protocol/
mcp-server.ts # MCP server setup (stdio transport)
tools.ts # Tool registry — imports compositeTools
types/
index.ts # Shared types (ToolDef, ToolContext, ToolResult)
utils/
rate-limiter.ts # Per-provider rate limiter
cache.ts # TTL cache for API responses
http.ts # Native fetch() wrapper with error handling
require-key.ts # API key validation helper
osv/ # 5 techniques govuln/ # 4 techniques
ghsa/ # 4 techniques depsdev/ # 10 techniques
nvd/ # 3 techniques scorecard/ # 2 techniques
epss/ # 4 techniques badge/ # 3 techniques
kev/ # 4 techniques libraries/ # 4 techniques
npm/ # 10 techniques clearlydefined/# 3 techniques
pypi/ # 4 techniques rekor/ # 5 techniques
crates/ # 4 techniques repology/ # 3 techniques
rubygems/ # 4 techniques typosquat/ # 2 techniques
nuget/ # 4 techniques meta/ # 1 technique
packagist/ # 4 techniques
go/ # 3 techniquesDesign decisions:
Composite architecture — 7 composite tools registered with MCP, each orchestrating multiple individual techniques. LLM context reduced from ~12K tokens (90 tool definitions) to ~1K tokens (7 composites).
22 providers, 1 server — Every data source is an independent module. Composite tools dispatch to the right provider techniques based on mode/ecosystem.
Per-provider rate limiters — Each data source has its own RateLimiter instance calibrated to that API's limits. No shared bottleneck.
TTL caching — Vulnerability data (5-30min), package metadata (5-15min), KEV catalog (60min), scorecard data (30min) are cached to avoid redundant API calls during multi-tool workflows.
Graceful degradation — Missing API keys don't crash the server. Tools return descriptive error messages: "Set LIBRARIES_API_KEY to enable Libraries.io tools."
2 dependencies — @modelcontextprotocol/sdk and zod. All HTTP via native fetch(). No external HTTP libraries needed.
Backward compatible — --tool CLI accepts both composite and individual technique names. --list-all shows all 90 techniques.
Libraries.io tools require a (free) API key from libraries.io
NVD free tier is limited to 5 requests per 30 seconds (50 with API key)
GHSA and Scorecard benefit from GITHUB_TOKEN for higher rate limits
CISA KEV is fetched as a single JSON file (~2MB) and cached for 60 minutes
Typosquatting detection compares against a curated list of top npm/PyPI packages (not exhaustive)
Go checksum database (sum.golang.org) may return 404 for very old or vendored-only modules
Repology API has strict rate limits and may throttle aggressive querying
ClearlyDefined coverage varies by ecosystem — npm and Maven have best coverage
macOS / Linux tested (Windows not tested)
Project | Domain | Tools |
Browser-based security testing | 39 tools, Firefox, injection testing | |
Cloud security (AWS/Azure/GCP) | 38 tools, 60+ checks | |
GitHub security posture | 39 tools, 45 checks | |
Vulnerability intelligence | 23 tools, 5 sources | |
OSINT & reconnaissance | 37 tools, 12 sources | |
Dark web & threat intelligence | 66 tools, 16 sources | |
Digital fingerprinting | 13 tools, 103 techniques | |
supply-chain-mcp-server | Software supply chain security | 7 tools, 90 techniques, 21 sources |
Find OpenSSF Best Practices badge by GitHub repository URL.
| Name | Required | Description | Default |
|---|---|---|---|
| repo_url | Yes | GitHub repository URL, e.g. 'https://github.com/kubernetes/kubernetes' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. Only states it finds a badge; no details on read-only nature, rate limits, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, no fluff – conveys the essential action and input concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool with one parameter, but lacks usage context and sibling differentiation beyond what's obvious.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter repo_url has good schema description and example. Schema coverage is 100%, so baseline 3 applies; description adds little extra semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it finds an OpenSSF Best Practices badge given a GitHub repo URL. Distinguishes from sibling tools like badge_project and badge_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly suggests use when you have a repo URL, but no explicit guidance on when not to use or alternatives like badge_project.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get OpenSSF Best Practices badge status and criteria for a project by ID.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Best Practices project ID number |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It only states the action (get badge) without disclosing behavioral traits like being read-only, error handling, or rate limits. The description is minimal and lacks transparency into side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, clear and front-loaded. Every word is necessary. No redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool (one param, no output schema), the description is adequate but incomplete. It mentions 'badge status and criteria' but does not describe the response format or what the criteria entail. Since there is no output schema, more detail on return values would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (one parameter 'id' described as 'Best Practices project ID number'). The description adds 'project ID' but does not provide additional meaning beyond the schema. Baseline is 3 because coverage is high, but no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Get OpenSSF Best Practices badge status and criteria for a project by ID.' It uses a specific verb ('Get') and resource ('project'), and distinguishes from sibling tools like 'badge_by_repo' and 'badge_search' by specifying 'by ID'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It does not mention any prerequisites, limitations, or when not to use it. Sibling tools exist but are not referenced for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search OpenSSF Best Practices badge projects.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (project name or keyword) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides no behavioral traits beyond the basic action. With no annotations, the burden is on the description to disclose aspects like result format (list of projects?), pagination, rate limits, or any side effects. None are mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one sentence) but lacks necessary detail. It is front-loaded with the core action, but brevity comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter, no output schema, no annotations), the description should at least indicate what the search returns (e.g., a list of badge project summaries). It does not, leaving the agent without enough context to properly use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the 'query' parameter has a description: 'Search query (project name or keyword)'). The tool description does not add any additional meaning beyond the schema, so it meets the baseline of 3 as per coverage guidelines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and the resource ('OpenSSF Best Practices badge projects'). It is specific enough to know what the tool does, but it does not differentiate from sibling tools like badge_by_repo or badge_project, which also relate to badge projects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it does not specify that this tool is for keyword-based searching, while badge_by_repo is for repository-based lookup and badge_project is for exact project name lookup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get security advisories for PHP packages from Packagist. Returns known vulnerabilities and CVEs affecting the specified package.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor name, e.g. 'symfony' | |
| name | Yes | Package name, e.g. 'http-kernel' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions returning vulnerabilities and CVEs but fails to disclose any behavioral traits such as required permissions, rate limits, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the core purpose. No unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 required params, no output schema), the description provides sufficient context: it identifies the return type (vulnerabilities/CVEs). However, it does not describe the result format or any pagination, but that is acceptable for this straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the individual parameter descriptions in the schema already document the parameters. The description adds no extra meaning beyond what the schema provides, warranting the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', resource 'security advisories', and scope 'for PHP packages from Packagist'. It effectively distinguishes the tool from siblings like composer_package or composer_search, which do not focus on vulnerabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives (e.g., deps_advisory, osv_query). It only implies usage for checking vulnerabilities, but lacks any when-not-to-use or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get PHP/Composer package metadata from Packagist including versions, description, maintainers, and repository information.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor/organization name, e.g. 'laravel' | |
| name | Yes | Package name, e.g. 'framework' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden. It correctly identifies this as a read operation fetching metadata from Packagist and lists included fields. However, it does not disclose potential issues like rate limits, authentication requirements, or what happens if the package does not exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose and output. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should explain return values. It lists included data types but lacks detail on structure or format. While adequate for a simple list tool, more context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both 'vendor' and 'name'. The description adds no extra parameter details beyond the schema, so it meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get'), the resource ('PHP/Composer package metadata from Packagist'), and lists specific data types included (versions, description, maintainers, repository info). This distinguishes it from sibling tools like composer_search (searching) and composer_stats (statistics).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching metadata of a known Composer package, but does not provide explicit guidance on when to use it versus siblings like composer_advisories or composer_search. No when-not-to-use or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Packagist for PHP packages matching a query string.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| page | No | Page number (default 1) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as pagination behavior, rate limits, or result format. For a search tool, this leaves significant ambiguity about how results are returned and whether there are limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that immediately conveys the tool's purpose and resource. No extraneous words or filler, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the absence of an output schema and annotations, the description provides adequate but minimal context. It does not explain the return format or any additional constraints beyond the query, leaving some gaps for a comprehensive understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for both parameters. The description repeats the query purpose but does not add extra meaning beyond what is already in the schema, such as specifying allowed characters or format. Therefore, it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches Packagist for PHP packages using a query string. It specifies the resource (Packagist) and the type of packages (PHP), distinguishing it from sibling tools that search other registries (e.g., npm, PyPI, RubyGems).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching PHP packages but lacks explicit guidance on when to use this tool versus alternatives like crate_search or gem_search. No when-not-to-use or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Packagist package download statistics including total, monthly, and daily download counts.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor name | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully inform about behavior. It correctly indicates a read operation and lists the output categories. However, it lacks details on authentication, rate limits, or whether the counts are raw or formatted. For a simple read tool, the baseline is acceptable but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the main action and key data points. Every word contributes to the understanding of the tool's purpose, with no unnecessary text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (two required parameters, no output schema), the description covers the core functionality. However, it does not mention the return format (e.g., JSON structure) or any potential limitations, which would help an agent fully understand the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for its two parameters ('vendor' and 'name'). The tool description does not add any additional meaning beyond what the schema already provides, so it meets the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Get'), the resource ('Packagist package download statistics'), and the specific data points ('total, monthly, and daily download counts'). It effectively distinguishes itself from sibling tools like composer_package (general package info) and composer_advisories (security advisories).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus its siblings. It does not mention alternatives or state that this tool is only for download statistics, leaving the agent to infer the context from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch dependencies for a specific crate version including dependency kind (normal/dev/build), version requirement, and optional flag.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Crate name | |
| version | Yes | Exact version, e.g. '1.0.193' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns dependency kind, version requirement, and optional flag, but does not mention read-only nature, error behavior, rate limits, or pagination. Some behavioral context is given but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that immediately states the core action. No wasted words. Could be structured with bullet points for clarity but effective as is.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must compensate. It lists some return fields but does not describe response structure, pagination, or error handling. Adequate for a simple query but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds no new meaning beyond the schema (e.g., it does not provide format constraints or additional context). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and resource 'dependencies for a specific crate version', and lists the included fields: dependency kind, version requirement, optional flag. This distinguishes it well from siblings like crate_info, crate_versions, and other deps tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you know a crate name and version, but offers no explicit when-to-use, when-not-to-use, or alternatives among the many sibling tools. It does not mention ecosystem (Rust) or differentiate from generic deps_dependencies tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch crates.io crate metadata including description, download counts, max version, repository, homepage, categories, and keywords.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Crate name, e.g. 'serde' or 'tokio' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description must disclose behavioral traits. It only states what data is fetched, omitting any mention of authentication, rate limits, error conditions, or side effects, even though the operation is simple and read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no filler, efficiently communicates purpose and output contents.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately lists returned information. Lacks mention of error handling or nonexistent crates, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter 'name' well-described in schema. Description adds context about returned fields but does not enhance parameter meaning beyond the schema, receiving baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Fetch' and identifies the resource 'crates.io crate metadata', listing concrete fields returned. It differentiates from sibling tools like crate_deps or crate_owners which cover different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies use when needing crate metadata, but does not provide explicit guidance on when not to use or suggest alternatives among many sibling tools like crate_deps or crate_versions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List owners of a crate on crates.io including login, name, URL, and kind (user/team). Useful for detecting ownership changes.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Crate name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes what is listed but does not clarify read-only nature, authentication needs, rate limits, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the tool's purpose and output fields. It is front-loaded with the core action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (1 parameter, no output schema), the description adequately covers the return fields and use case. It does not mention ordering or pagination but is sufficient for a basic list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the parameter 'name' described as 'Crate name'. The description adds no further semantic detail beyond the schema, meeting but not exceeding the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'owners of a crate', and enumerates the fields returned (login, name, URL, kind). This distinguishes it from sibling tools like crate_info or crate_versions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is 'useful for detecting ownership changes', providing a use case. However, it does not specify when to use this tool over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all versions of a crate with version number, yanked status, license, crate size, creation date, and download count.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Crate name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only lists output fields but does not mention if all versions are returned (pagination limits), authentication needs, rate limits, or any ordering/filtering behavior. This leaves significant gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 19 words, front-loaded with the action and resource, and efficiently lists the fields. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description provides the output fields but lacks explicit structure (e.g., array vs. object) and any mention of ordering, filtering, or limits. It is functional but not fully complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (one parameter 'name' with basic description 'Crate name'). The description does not add any additional meaning beyond the schema, such as format, constraints, or examples. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all versions of a crate' and enumerates the specific fields returned (version number, yanked status, license, crate size, creation date, download count). This distinguishes it from siblings like crate_info (single crate info) and crate_deps (dependencies).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., crate_info, crate_deps). It does not mention scenarios where this is appropriate or not, nor does it point to sibling tools for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a security advisory by key (e.g. GHSA-xxxx-xxxx-xxxx) from deps.dev
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Advisory key, e.g. "GHSA-xxxx-xxxx-xxxx" or CVE ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only states 'Fetch', which implies a read operation, but does not mention auth requirements, rate limits, or any side effects. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that conveys the essential purpose. It is concise and front-loaded, though it lacks any structural elements like bullet points or expanded details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides minimal context. It does not mention return format, error handling, or source reliability, leaving some gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the single parameter 'key' with an example format. The description adds no additional meaning beyond what the schema provides, so baseline score of 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch'), the resource ('security advisory'), and the source ('from deps.dev'), with a specific example of the key format. This distinguishes it from sibling tools that focus on packages, versions, or other data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to fetch an advisory by key, but provides no guidance on when to use this over related advisory tools like ghsa_id or osv_id. No when-not-to-use or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the dependency tree for a specific package version from deps.dev
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility for explaining behavior. It only states the function without disclosing rate limits, authentication needs, response structure, or side effects (none expected).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that conveys the core purpose without unnecessary words. It is front-loaded and efficient, though it could be slightly expanded for completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description lacks details about the dependency tree's structure (e.g., whether it includes transitive dependencies, version constraints). This is insufficient for an agent to fully understand what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing clear parameter descriptions. The tool description does not add additional meaning beyond the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves the dependency tree for a specific package version, specifying the source (deps.dev). This distinguishes it from sibling tools like deps_package (package info) and deps_dependents (reverse dependencies).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as deps_dependents or deps_version. The description does not mention prerequisites, restrictions, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get packages that depend on a specific package version from deps.dev
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It indicates a read operation ('Get') and states the data source ('from deps.dev'), but does not mention rate limits, authentication requirements, or the nature of the response (e.g., pagination). Behavior is adequately implied for a simple query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence of 11 words with no redundancy. It delivers the core purpose without extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema, so the description should provide hints about the return format (e.g., list of package names). It does not, leaving agents uncertain about the response structure. Given low complexity and three required parameters, the omission is a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are fully described in the input schema (100% coverage). The description adds no additional meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (Get), the resource (packages that depend), and the specific context (on a specific package version from deps.dev). It effectively distinguishes from sibling tools like deps_dependencies which gets dependencies, not dependents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for finding dependents of a specific package version but does not provide explicit guidance on when to use it versus alternatives like libraries_dependents. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a package on deps.dev to get metadata, versions, and security information
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It only says what the tool returns, omitting details about side effects, error handling, rate limits, or required permissions. Important behavioral context is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded. It efficiently conveys the core purpose without unnecessary words, though it could benefit from additional structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with two required parameters and no output schema, the description covers basic purpose but lacks details on return format, error conditions, or limitations. It is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described clearly. The description adds no additional meaning beyond the schema, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up a package on deps.dev to get metadata, versions, and security information. It uses a specific verb and resource, but does not differentiate from sibling tools that also deal with deps.dev packages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like deps_dependencies or deps_advisory. The description only states what it does without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get project information from deps.dev by repository URL (e.g. github.com/expressjs/express)
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Repository URL without https:// prefix, e.g. "github.com/expressjs/express" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose the nature of the operation (read-only), what information is returned, or any side effects. The agent is left to assume basic GET behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, clear sentence with no fluff. The example is front-loaded and the entire description is highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 1 parameter and no output schema, the description is sufficient to use the tool. However, it lacks behavioral details that would be helpful for an agent, such as typical output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for the 'url' parameter. The description adds value by specifying the format (without https:// prefix) and providing an example, which aids correct invocation beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb 'Get', the resource 'project information from deps.dev', and the input method 'by repository URL'. The example distinguishes it from sibling tools like deps_package (which takes a package name).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a repository URL, but does not explicitly state when to use this tool versus alternatives (e.g., deps_package). No when-not or exclusion guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a package by Package URL (purl) on deps.dev. Purl format: pkg:ecosystem/name@version
| Name | Required | Description | Default |
|---|---|---|---|
| purl | Yes | Package URL, e.g. 'pkg:npm/express@4.18.2' or 'pkg:pypi/requests@2.31.0' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description states it's a lookup, implying read-only. Lacks details on potential limits, errors, or side effects, but straightforward operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with format example, highly concise and front-loaded. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simplicity (1 parameter, no output schema), the description adequately covers purpose and parameter format. No missing context for operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with parameter description and examples, but description adds format guidance and example purls, enhancing understanding beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Look up a package by Package URL (purl) on deps.dev', with specific verb and resource. Distinguishes from siblings like deps_package by specifying the lookup method.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly explains the purl format and provides examples, indicating when to use this tool (when you have a purl). Does not explicitly mention alternatives or exclusions, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a package by its SHA256 artifact hash on deps.dev
| Name | Required | Description | Default |
|---|---|---|---|
| hash | Yes | SHA256 hash of the package artifact |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It does not mention any behavioral traits such as whether the operation is read-only, rate limits, error behavior for missing hashes, or permissions needed. The description only states the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is front-loaded with the action and immediately conveys the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is largely complete. It tells the agent what the tool does and what input is required. However, it could mention what is returned (e.g., package details) to further enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with the 'hash' parameter already described as 'SHA256 hash of the package artifact'. The tool description does not add any additional meaning or usage details beyond the schema, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'look up', the resource 'package', and the method 'by its SHA256 artifact hash'. It effectively distinguishes from sibling tools like deps_package (which likely uses package name) by specifying the hash-based lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for looking up packages by hash, but it does not provide explicit guidance on when to use this tool over alternatives (e.g., deps_purl_lookup). No exclusions or context about when not to use it are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the requirements (version constraints) for a specific package version from deps.dev
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states the tool gets requirements but does not explain authentication needs, rate limits, error behavior (e.g., for non-existent packages), or the structure of the returned data. Essential traits for a retrieval tool are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words. It is front-loaded with the main action. However, its brevity sacrifices necessary context, making it somewhat under-specified. It earns a 4 for efficiency but loses a point for leaving key details to the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, and the existence of many sibling deps tools, the description is insufficient. It does not clarify what 'requirements' entails, whether it returns a list or object, or how it differs from deps_dependencies. The description leaves significant gaps for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters (ecosystem, name, version) with descriptions, achieving 100% coverage. The description adds no additional semantic value beyond the schema, as it only mentions using 'version' implicitly. Per guidelines, the baseline is 3 when schema coverage is high, and no extra value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves 'requirements (version constraints)' for a specific package version from deps.dev. The verb 'Get' and resource 'requirements' are specific, and the mention of 'version constraints' distinguishes it from similar sibling tools like deps_dependencies and deps_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as deps_dependencies or deps_version. It does not mention any prerequisites, context, or exclusions, leaving the agent to infer usage without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find similarly named packages on deps.dev for typosquatting detection. Useful for verifying you're using the legitimate package.
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name to find similar names for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as how similarity is determined, number of results, or whether it makes external API calls. The description is too minimal for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, front-loading the purpose and usage. Every word is necessary; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two simple parameters and no output schema, the description is adequate but lacks detail on output format or usage nuances. For a security-related tool, more context could be beneficial, but it meets minimum viability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds no additional meaning beyond the schema; it merely restates the tool's purpose. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Find' and the resource 'similarly named packages' with a specific purpose 'for typosquatting detection'. It distinguishes from sibling tools like 'typosquat_check' and 'typosquat_compare', which have different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Useful for verifying you're using the legitimate package', providing clear context for use. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tool list implies other options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed info about a specific package version from deps.dev including links, licenses, and advisories
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pypi, cargo, go, maven, nuget) | |
| name | Yes | Package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description mentions the type of data returned (links, licenses, advisories), but lacks details on side effects, authentication, rate limits, or response structure. For a read-only query tool, the description adds some transparency beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise, and front-loaded with the action ('Get detailed info'). No unnecessary words. Could potentially add more detail without losing conciseness, but current structure is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description should compensate by detailing the return value. It mentions three categories of data but not structure or fields. Given the complexity of sibling tools, more context would help, but the description is adequate for a simple version lookup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema describes all 3 parameters with full coverage. The description does not add extra meaning beyond the schema, such as allowed ecosystem values or version format conventions. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get detailed info' and the resource 'specific package version from deps.dev', listing included data (links, licenses, advisories). It differentiates from sibling tools like deps_package or deps_advisory by focusing on a single version's details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like deps_dependencies or deps_advisory. The description does not specify prerequisites, when not to use it, or scenarios where other tools are preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find CVEs with EPSS score above a given threshold.
| Name | Required | Description | Default |
|---|---|---|---|
| threshold | Yes | EPSS threshold (0-1), e.g. 0.5 for top 50% | |
| limit | No | Max results (default 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose behavioral traits such as pagination, response format, or rate limits. For a tool with no annotations, the description should provide more context about what happens during execution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It is maximally concise while conveying the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple threshold filter but lacks information about the output (e.g., list of CVE IDs or full details). Without an output schema, the description should hint at the return structure for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with comprehensive parameter descriptions. The description adds no additional meaning beyond restating the threshold concept, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Find'), the resource ('CVEs'), and the criteria ('EPSS score above a given threshold'). It effectively distinguishes from sibling tools like epss_score (single lookup) and epss_top (top scores) by focusing on threshold-based filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (threshold filtering) but does not explicitly mention alternatives or when not to use it. No guidance on choosing between this and siblings like epss_batch or epss_top.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Batch EPSS scores for multiple CVEs (up to 100) in a single request.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_ids | Yes | Array of CVE IDs (max 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it fetches 'EPSS scores' but does not clarify if it is read-only, destructive, or any side effects. While likely a safe read operation, the description lacks explicit behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. Every phrase contributes essential information about the tool's capability and constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 param, no output schema) the description is nearly complete. It mentions the batch nature and limit, but missing details about the response format (e.g., returns a mapping of CVEs to scores) which could be inferred from sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds 'batch' and 'single request' context but does not add meaning beyond the schema's description of 'Array of CVE IDs (max 100)'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Batch EPSS scores' and the resource 'multiple CVEs' with a specific limit of 100. It distinguishes itself from sibling tools like 'epss_score' (single CVE) and 'epss_above_threshold' (querying).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for batch requests instead of individual ones via 'up to 100' and 'single request', but does not explicitly mention when not to use it or alternatives. However, the sibling context provides implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get EPSS exploit probability and percentile for a single CVE ID.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE ID, e.g. 'CVE-2024-3094' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes core action but does not disclose return format, rate limits, or authentication needs. Adequate for a simple query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no wasted words, front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Minimal description for a simple tool with one param and no output schema. Could specify return structure (probability, percentile) but adequate for domain users.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter with schema description covering it fully. Description adds no new meaning beyond the schema. Baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Get' and specific resource 'EPSS exploit probability and percentile for a single CVE ID'. Distinct from siblings epss_batch and epss_top by specifying 'single'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly advises use for a single CVE ID via the word 'single', distinguishing from epss_batch for multiple IDs. No explicit when-not or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the highest EPSS-scoring CVEs (most likely to be exploited).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (default 25, max 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states the tool retrieves top EPSS CVEs but does not disclose output format (e.g., fields returned) or if it requires any prerequisites. The limit parameter is partially detailed in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, zero wasted words. Front-loaded with the action and resource. Highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is sufficiently informative. It could optionally mention that results include CVE IDs and EPSS scores, but the current brevity is adequate given low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description of the limit parameter (default 25, max 100). The description adds no extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns the highest EPSS-scoring CVEs, using a specific verb and resource. It distinguishes from siblings like epss_above_threshold (threshold-based) and epss_score (specific CVEs) through the name and wording, though not explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as epss_above_threshold or epss_score. The description does not mention alternative use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch RubyGems gem metadata including name, version, authors, description, download counts, project URI, source code URI, and other package details.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Gem name, e.g. 'rails' or 'devise' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It describes a read-only operation but doesn't explicitly state safety, permissions, or potential errors. Missing details like pagination or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded with purpose. Every word adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately lists returned metadata. However, could mention request format or any constraints like gem existence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'name'. The description adds general context about return fields but doesn't provide additional parameter-specific meaning beyond the schema example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches RubyGems gem metadata, listing specific fields. It distinguishes from sibling tools like gem_search, gem_versions, and gem_reverse_deps by focusing on metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. For instance, if only download counts are needed, gem_search might be more appropriate. The description implies usage for metadata but lacks exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get reverse dependencies of a RubyGems gem — lists all gems that depend on this gem. Useful for assessing blast radius of a compromised package.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Gem name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the tool lists all gems that depend on the input gem, but lacks details on pagination, rate limits, or completeness. Behavioral disclosure is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool, the description covers the basic purpose and a use case. However, without an output schema, it would benefit from briefly explaining the return format. Additionally, no mention of potential limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'name' as 'Gem name'. The tool description does not add meaning beyond this, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states the tool gets reverse dependencies for a RubyGems gem and lists dependent gems. It clearly distinguishes from sibling tools like gem_info, gem_search by focusing on reverse dependencies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides a use case (assessing blast radius) but does not explicitly state when to use versus alternatives or any exclusions. Context is implied rather than directive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the RubyGems registry for gems matching a query string.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| page | No | Page number (default 1) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the basic search behavior without disclosing pagination, rate limits, or return format. Minimal behavioral context beyond the operation itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is direct and concise. Every word serves a purpose with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool without output schema, the description is adequate but could mention that it returns a list of gems with details. No output schema is present, so the return content is entirely unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with both parameters described. The description adds no extra meaning beyond the schema; it does not specify query syntax or page handling. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly specifies the verb 'search', resource 'RubyGems registry', and the action of finding gems matching a query string. It distinguishes from sibling tools like gem_info and gem_versions which are for specific gems.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it searches for gems, but it does not explicitly state when to use this tool versus alternatives like gem_info or gem_versions. No exclusions or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all versions of a RubyGems gem with release dates, platform info, and version numbers.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Gem name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must cover behavioral traits. It implies a read-only listing but does not explicitly state side effects, authorization needs, or rate limits. For a simple list tool, the disclosure is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with zero wasted words. Every element (action, resource, what's included) is present and efficiently communicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one required parameter, no output schema), the description sufficiently explains what the tool returns and its purpose. No missing information hinders agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the only parameter ('name' with description 'Gem name') at 100% coverage. The tool description adds no additional meaning or format details beyond what the schema provides, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (List), resource (versions of a RubyGems gem), and included data (release dates, platform info, version numbers). It effectively distinguishes this tool from siblings like gem_info or gem_search by focusing on version listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs alternatives. It does not mention when-not-to-use or suggest other tools for different needs, leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a GitHub Security Advisory by its GHSA or CVE identifier.
| Name | Required | Description | Default |
|---|---|---|---|
| ghsa_id | Yes | Advisory identifier (e.g. GHSA-xxxx-xxxx-xxxx or CVE-2023-1234) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as rate limits, authentication needs, or the structure of the returned data. For a read operation without output schema, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence that is front-loaded with the action and resource, containing no unnecessary words. It is concise and direct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain the return value, but it does not. The tool is simple, but completeness is lacking for an agent to understand what information is retrieved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% as the parameter has a description in the schema. The tool description essentially repeats the schema's parameter description, adding no extra meaning beyond the format examples. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a GitHub Security Advisory by identifier. The verb 'Fetch' and resource 'GitHub Security Advisory' are specific. It distinguishes from siblings like ghsa_search and ghsa_recent by specifying retrieval by a single identifier.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when you have an advisory identifier but provides no explicit guidance on when to use this tool over alternatives like ghsa_search or ghsa_recent. No when-not-to-use or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List GitHub Security Advisories affecting a specific package in a given ecosystem.
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (npm, pip, go, rubygems, nuget, maven, composer, rust, pub, erlang, actions, swift) | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It only says 'List' but fails to disclose pagination, rate limits, or whether it returns all advisories or only recent ones. For a listing tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, well-structured sentence with no wasted words. Purpose is immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no annotations. The description does not explain what the output contains (e.g., list of GHSA IDs, titles, severity). Without this, the agent cannot fully understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters already described. The description adds no extra meaning beyond the schema, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists GitHub Security Advisories for a specific package in a given ecosystem. Distinguishes from siblings like ghsa_id (single advisory) and ghsa_search (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied (when you have a package name and ecosystem), but no explicit when-not-to-use or alternatives are provided. Could mention that this is for package-specific queries vs. general search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the most recently updated GitHub Security Advisories.
| Name | Required | Description | Default |
|---|---|---|---|
| per_page | No | Number of results to return (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully convey behavioral traits. It does not mention pagination, rate limits, read-only nature, or return format. The description is minimal and lacks transparency about important behaviors beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that conveys the essential purpose without unnecessary words. It is front-loaded and every part is functional.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one optional parameter, no output schema), the description sufficiently explains what it does. It could mention that it returns a list of advisories, but the word 'list' implies that. Overall, it is nearly complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (per_page) with 100% description coverage. The tool description adds no additional meaning beyond what the schema already provides. Baseline of 3 is appropriate since schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'GitHub Security Advisories', with the qualifier 'most recently updated' to distinguish from other ghsa tools like ghsa_search or ghsa_id. It is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings (e.g., ghsa_search, ghsa_id). There are no explicit when-to-use, when-not-to-use, or alternative recommendations, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search GitHub Security Advisories by keyword, ecosystem, and severity.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Search keyword | |
| ecosystem | No | Package ecosystem filter (npm, pip, go, rubygems, nuget, maven, composer, rust, pub, erlang, actions, swift) | |
| severity | No | Severity filter (low, medium, high, critical) | |
| per_page | No | Number of results to return (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It does not mention that the tool performs a read-only search, return format (list), pagination behavior, or any authentication requirements. The per_page parameter hints at pagination but is not described in the context of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is front-loaded with the action and resource, but lacks detail that would improve completeness without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, no output schema, no annotations), the description is insufficient. It does not explain the result format, pagination, or how the search behaves (e.g., keyword matching). The tool is usable but leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with descriptions for all parameters. The description adds no additional meaning beyond what the schema already provides, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches GitHub Security Advisories and lists the filtering dimensions (keyword, ecosystem, severity). It uses a specific verb and resource, and is distinguishable from related sibling tools like ghsa_id (specific ID) and ghsa_package (package-specific).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description does not mention when not to use it or suggest alternative tools for specific use cases like looking up a single advisory by ID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch Go module info from the module proxy: latest version and all available versions. Module paths are automatically encoded for the proxy.
| Name | Required | Description | Default |
|---|---|---|---|
| module | Yes | Go module path, e.g. 'golang.org/x/net' or 'github.com/gin-gonic/gin' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses automatic encoding of module paths and that it retrieves both latest and all versions. Lacks mention of read-only nature or error handling, but sufficient for a simple fetch tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff, front-loaded with core action. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 param, no output schema), description covers what the tool does and a key behavioral detail. No obvious gaps for expected use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good description for the module parameter. Description adds value by noting automatic path encoding, which is not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool fetches Go module info (latest and all versions) from the module proxy. Uses specific verb and resource. Distinguishes from siblings like go_version or go_vuln_by_module which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. Context is clear (fetch module proxy info), but with many sibling tools, alternative recommendations are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a Go module version in the checksum database (sum.golang.org) for hash verification. Returns the checksum database entry.
| Name | Required | Description | Default |
|---|---|---|---|
| module | Yes | Go module path | |
| version | Yes | Version string, e.g. 'v1.20.0' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions 'returns the checksum database entry' but fails to disclose behavioral traits such as read-only nature, network dependency, or any side effects. Minimal behavioral context is provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that are front-loaded with essential information. No redundant or extraneous text. Every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with 2 well-documented parameters, the description provides adequate context. However, the lack of output schema means the return value is only vaguely described ('entry'), which could be more precise.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already documented in the schema. The description does not add further meaning beyond the schema's descriptions. Baseline of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Look up'), the resource ('Go module version in the checksum database'), and the purpose ('for hash verification'). It distinguishes itself from sibling tools like go_module and go_version by specifying the checksum database context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for hash verification but does not explicitly state when to use this tool over alternatives like go_module or go_version. No guidance on prerequisites or exclusions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch info and go.mod contents for a specific Go module version. Returns version metadata and parsed dependency list from go.mod.
| Name | Required | Description | Default |
|---|---|---|---|
| module | Yes | Go module path | |
| version | Yes | Version string, e.g. 'v1.20.0' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes a read-only fetch operation but does not explicitly state that it is non-destructive, nor does it mention any side effects, rate limits, or authentication requirements. While the read-only nature is implied, additional behavioral context (e.g., cache behavior, freshness) is absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences totaling about 20 words, with no redundant information. It front-loads the core action ('Fetch info and go.mod contents') and then specifies the return values. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema, the description compensates by stating it returns 'version metadata and parsed dependency list from go.mod,' which provides sufficient high-level context. However, it could detail the structure of the dependency list or mention whether multiple versions are handled. Overall, it is mostly complete for a simple fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage for both parameters (module and version). The description adds no extra meaning beyond the schema, such as format constraints or examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches info and go.mod contents for a specific Go module version, and explicitly indicates it returns version metadata and a parsed dependency list. This distinguishes it from siblings like go_module (which may fetch module info without version specificity) and go_vuln_by_module (which focuses on vulnerabilities).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like go_module, go_sum, or other version-specific tools. It lacks explicit 'when to use' or 'when not to use' instructions, leaving the agent to infer context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find Go vulnerabilities affecting a specific module. Fetches the modules index and filters for entries matching the given module path.
| Name | Required | Description | Default |
|---|---|---|---|
| module | Yes | Go module path, e.g. 'golang.org/x/net' or 'github.com/gin-gonic/gin' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes a fetch-and-filter operation but omits details like whether it's read-only, rate limits, error handling, or if the module index is cached. The lack of transparency is notable for a query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no redundant or extraneous information. The description is front-loaded with the core action and efficiently completes with the method.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should explain return format or behavior (e.g., list of vulnerabilities, empty results). It only says 'filters for entries' without specifying what is returned. Missing context on error cases or pagination leaves the agent with incomplete information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'module' already described in the schema as a Go module path with examples. The tool description adds minimal extra meaning beyond confirming it filters by that path. Baseline 3 is appropriate as schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds Go vulnerabilities for a specific module, with a precise verb ('Find') and resource ('vulnerabilities affecting a specific module'). It distinguishes from siblings like go_vuln_id (by ID) and go_vuln_list (list all) by focusing on module path filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a module path, but provides no explicit guidance on when not to use it or alternatives. No comparisons with siblings like go_vuln_id or go_vuln_list are given, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Go Vulnerability Database metadata including last modified time.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It implies a read-only operation by saying 'Get' and specifies the returned data (last modified time), but does not disclose potential behavior such as caching, rate limits, or required authentication. The description is adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded. Every word adds value, with no filler or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (no parameters, no output schema), the description is mostly complete. It states what the tool returns (metadata including last modified time). However, it could hint at related tools or typical usage context, which would make it more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and the schema coverage is 100% (since no parameters exist). According to the guidelines, a score of 4 is appropriate for a tool with zero parameters, as the description does not need to add parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'Go Vulnerability Database metadata' including a specific data point ('last modified time'). It distinguishes itself from sibling Go vulnerability tools like go_vuln_by_module and go_vuln_id by focusing on database-level metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives. For example, it could mention that this tool is useful for checking the freshness of the database before querying specific vulnerabilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a Go vulnerability by its ID from the Go Vulnerability Database. Returns advisory details, affected modules, versions, and references.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Go vulnerability ID, e.g. 'GO-2024-2824' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states return data but omits behavioral traits such as authentication requirements, rate limits, or error handling (e.g., behavior for invalid IDs). Adequate for a simple fetch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the action and resource, with no redundant information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers return contents sufficiently. However, it lacks details on error scenarios (e.g., not found or invalid ID) and does not reference any pagination or limits. Still good for a simple lookup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and includes an example value ('GO-2024-2824'). The description adds no additional meaning beyond what the schema already provides for the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a Go vulnerability by its ID from the Go Vulnerability Database and specifies return contents (advisory details, modules, versions, references). It distinguishes from sibling tools like go_vuln_by_module and go_vuln_list through the precise focus on ID lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a Go vulnerability ID, but does not explicitly mention when not to use it or suggest alternatives like go_vuln_by_module for module-based queries. Only indirect context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all Go vulnerability IDs from the database index. Returns an array of entry objects with id and modified fields.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states it lists IDs and returns an array, but does not mention any side effects, rate limits, authorization needs, or behavior beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences pack all essential information without any fluff. The description is front-loaded and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter list tool, the description is complete: it specifies what is listed and what fields are returned. However, it might benefit from clarifying the scope (e.g., 'all' vs filtered) or mentioning pagination if applicable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description cannot add meaning beyond the schema. Baseline is 4 for no parameters and 100% schema coverage. The description correctly notes there are no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all Go vulnerability IDs from the database index and returns an array with id and modified fields. The verb 'list' and resource are specific. However, it does not differentiate from similar siblings like go_vuln_by_module or go_vuln_id, which could cause confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or context for choosing this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if a CVE is in the CISA Known Exploited Vulnerabilities (KEV) catalog.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE ID to check, e.g. 'CVE-2024-3094' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or error handling. The tool appears safe (read-only lookup), but this is only implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no extraneous words. It efficiently conveys the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with no output schema, the description lacks information about the return format (e.g., boolean or details) and error handling. This is a notable gap given the minimal complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear parameter description and example. The description adds contextual meaning about checking KEV catalog but does not enhance parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks if a CVE is in the KEV catalog, with a specific verb ('check') and resource ('CVE in KEV catalog'). It is distinct from sibling tools like kev_search and kev_recent, though not explicitly contrasted.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as kev_search or kev_recent. The description only states the function without context on bulk checks or other scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get recently added KEV entries within the last N days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to look back (default 30) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It indicates a read operation ('Get') but does not disclose ordering, pagination, or response format. Adequate for a simple retrieval but lacks behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with the verb and resource. No extraneous words, efficiently conveys core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description is minimally sufficient. However, it omits details about what is returned (e.g., list of entries, full details). Completeness is adequate but not rich.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the 'days' parameter (default 30 and meaning). The tool description does not add additional semantics beyond what the schema already provides, so baseline score applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Get', resource 'recently added KEV entries', and constraint 'within the last N days'. This distinguishes it from sibling tools like kev_lookup, kev_search, and kev_stats, which have different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as kev_search or kev_lookup. The description does not specify context or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search KEV entries by keyword (matched against vendor, product, name, description).
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Search keyword (matched against vendor, product, name, description) | |
| limit | No | Max results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility. It only states the matching behavior but does not mention that it's a read-only operation, any authentication requirements, pagination details, or result format. The description is insufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with two parameters and no output schema, the description covers the core purpose. However, it omits details like default limit behavior, return format, and error handling, leaving room for ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% as both parameters have descriptions in the schema. The tool description adds value by specifying that keyword matches against vendor, product, name, and description, which is not explicitly detailed in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (search), the resource (KEV entries), and the matching criteria (keyword against vendor, product, name, description). It distinguishes from sibling tools like kev_lookup and kev_recent by indicating a broad search capability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like kev_lookup or kev_recent. The name 'search' implies it's for general keyword searches, but no exclusions or context are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get KEV catalog statistics: total count, top vendors, entries per year, and ransomware usage breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It implies a read-only operation ('Get'), but does not discuss side effects, authentication, rate limits, or data freshness. For a simple stats endpoint, some context is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence (16 words) that immediately conveys the tool's output. It is front-loaded with the main action ('Get KEV catalog statistics') and lists the specific breakdowns, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and no annotations, the description provides adequate context about the return values (total count, top vendors, entries per year, ransomware breakdown). It does not mention output format or pagination, but the simplicity of the tool makes this acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema description coverage is 100%. The description adds no parameter info because none exist. Baseline for 0 parameters is 4, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to 'Get KEV catalog statistics' including specific breakdowns (total count, top vendors, entries per year, ransomware usage). This distinguishes it from sibling tools like kev_lookup (individual lookups) and kev_search (searching).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for aggregate statistics and overview of the KEV catalog. It does not explicitly exclude alternative tools, but the purpose is clear enough that an agent can infer when to use this tool over siblings like kev_recent or kev_search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get packages that depend on a specific package from Libraries.io
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Package platform, e.g. "NPM", "Pypi", "Cargo", "Go", "Maven", "NuGet" | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits beyond the basic function. No mention of pagination, rate limits, or result format, which are important for a tool that likely returns lists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, concise sentence that directly states the purpose with no unnecessary words. Front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two required string inputs and no output schema, the description is adequate. However, it could mention that the result is a list of dependent packages, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with both parameters having descriptions. The tool description adds no extra meaning beyond the schema's parameter descriptions, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Get' and the resource 'packages that depend on a specific package', differentiating it from sibling tools like libraries_deps (dependencies) and libraries_package (package info).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives among the many sibling tools. Description does not mention when not to use it or provide any usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get dependencies for a specific package version from Libraries.io
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Package platform, e.g. "NPM", "Pypi", "Cargo", "Go", "Maven", "NuGet" | |
| name | Yes | Package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states purpose. Does not disclose rate limits, authentication, data freshness, or whether dependencies are direct or transitive. Minimal behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise single sentence, but it is under-specified. Could be improved by adding key details without becoming verbose. The brevity sacrifices substance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description fails to explain the return format, error behavior, or scope of dependencies. Incomplete for reliable agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema provides full descriptions for all three parameters (100% coverage). Description adds no additional semantic value beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb ('Get dependencies'), resource ('package version'), and source ('Libraries.io'). It is specific enough to distinguish from many sibling tools that target specific ecosystems.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like platform-specific tools (e.g., crate_deps, gem_versions). Lacks context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get package metadata from Libraries.io including repository info, versions, and popularity metrics
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Package platform, e.g. "NPM", "Pypi", "Cargo", "Go", "Maven", "NuGet", "Rubygems" | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states the tool 'gets' metadata, implying a read operation, but does not mention any side effects, authentication requirements, rate limits, or error behavior. The lack of detail on what happens when inputs are invalid or results are empty is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core purpose without unnecessary words. Every word adds value, and the structure is clean and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description partially addresses return values by listing example content (repository info, versions, popularity metrics). However, it does not specify the exact format, pagination, or limits. For a simple lookup with few parameters and no nested objects, the description is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters ('platform' and 'name') have descriptions in the schema, which already provide clear semantics. The description adds context about the output (repository info, versions, popularity metrics) but does not elaborate on the parameters themselves. With 100% schema coverage, the description meets the baseline without adding significant new meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves package metadata from Libraries.io, including specific details like repository info, versions, and popularity metrics. The verb 'Get' and the resource 'package metadata' are precise, and the tool name distinguishes it from sibling tools that focus on specific package managers or other Libraries.io functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching package metadata from Libraries.io but lacks explicit guidance on when to use this tool versus specific package manager tools (e.g., npm_package, pypi_package). No alternatives or exclusions are mentioned, leaving the agent to infer context from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the SourceRank quality score breakdown for a package from Libraries.io
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Package platform, e.g. "NPM", "Pypi", "Cargo", "Go", "Maven", "NuGet" | |
| name | Yes | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose any behavioral traits such as whether the operation is read-only, requires authentication, or has rate limits. The description offers no additional context beyond the tool's purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that efficiently conveys the tool's purpose. No unnecessary words or information are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description should provide hints about the return format or content. It does not mention what the breakdown includes, possible values of SourceRank, or any additional contextual information, making it incomplete for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully documents both parameters ('platform' and 'name') with descriptions. The tool description adds no extra meaning beyond what the schema provides, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the specific resource ('SourceRank quality score breakdown for a package'), and it specifies the data source ('Libraries.io'). This distinguishes it from sibling tools like libraries_dependents and libraries_package, which focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention use cases, prerequisites, or exclusions, leaving the agent to infer appropriate usage without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Batch license lookup for multiple components via ClearlyDefined.
| Name | Required | Description | Default |
|---|---|---|---|
| coordinates | Yes | Array of ClearlyDefined coordinates, e.g. ['npm/npmjs/-/express/5.1.0', 'pypi/pypi/-/requests/2.31.0'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only mentions the method ('via ClearlyDefined') but omits error handling, response behavior, or any side effects. Insufficient detail for a batch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, 10 words, front-loaded with the key action. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite good parametric documentation, the description lacks return value information (no output schema) and behavioral details like handling of partial failures. Adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'coordinates' parameter well. The description adds no additional semantic meaning beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('lookup'), resource ('license'), and scope ('batch'), clearly distinguishing from single-lookup tools like license_lookup. No tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The term 'batch' implies use for multiple components, but there is no explicit guidance on when to use this versus alternatives, nor any mention of when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get curated license data for a software component from ClearlyDefined.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Package type: npm, pypi, maven, nuget, gem, crate, go, etc. | |
| name | Yes | Package name (use namespace/name for scoped packages) | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It indicates a read operation ('Get curated license data') but does not disclose side effects, authentication, or what 'curated' implies. Acceptable but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no fluff, conveys core purpose immediately. Optimal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, and description does not hint at output structure (e.g., license identifier, text). Missing details on return format or behavior for exact version matching. Incomplete for a tool with no annotations or output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with descriptions for all three parameters (type, name, version). Description adds no additional meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Get', resource 'license data', and source 'from ClearlyDefined'. It distinguishes from siblings which perform different actions like badge, search, or batch operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like 'license_batch' or 'license_search'. No mention of prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search ClearlyDefined for components by pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | Yes | Search pattern, e.g. 'express' or 'react' | |
| type | No | Filter by type: npm, pypi, maven, etc. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the basic action without disclosing behavioral traits such as authentication needs, rate limits, error handling, or what happens when no results are found.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently communicates the core action and scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description does not explain what the search returns (e.g., license data, summary). It also lacks context about 'ClearlyDefined' and what 'components' means. The tool name hints at licenses, but this is not leveraged.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing clear descriptions for both parameters. The tool description adds no extra meaning beyond the schema, so baseline score 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'ClearlyDefined for components', and the method 'by pattern'. It distinguishes this tool from siblings like 'npm_search' or 'pypi_search' by specifying the service and component focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The agent is left without context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch full Sigstore attestation bundles for an npm package version. Returns SLSA provenance and publish attestations when available.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name, e.g. 'express' or '@scope/pkg' | |
| version | Yes | Exact semver version, e.g. '4.18.2' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates the tool is read-only ('Fetch') and returns specific data (SLSA provenance and publish attestations) conditionally ('when available'). However, it does not disclose potential size of responses, error conditions, or any authentication/rate limit considerations. Since no annotations are provided, more details would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences: the first states the action and resource, the second adds return details. Every word adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description should explain the return structure in detail. It only mentions 'SLSA provenance and publish attestations' without describing the format, fields, or potential contents of the attestation bundles. This is insufficient for an agent to process the response correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both parameters (name and version). The description adds minimal extra meaning beyond the schema, mentioning 'full Sigstore attestation bundles' but not tying it to parameters. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Fetch' and identifies the resource as 'full Sigstore attestation bundles' for an npm package version. It clearly states what the tool does, but it does not explicitly distinguish it from the sibling tool 'npm_provenance', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like npm_provenance. No when-not-to-use or prerequisite information is provided, leaving the agent without decision-making context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Bulk query npm security advisories for a set of packages and versions. Provide an object mapping package names to version arrays, e.g. {"express":["4.17.1"]}.
| Name | Required | Description | Default |
|---|---|---|---|
| packages | Yes | Object of package names to arrays of versions, e.g. {"express":["4.17.1"]} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions a query operation (read-only) but lacks details on authentication, rate limits, or any side effects. With no annotations provided, the description carries full burden and falls short.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no unnecessary words. The example is directly useful. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers the input well but does not describe the output (e.g., returns list of advisories). This is a notable gap for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the 'packages' parameter, but the description adds concrete formatting guidance with an example ('{"express":["4.17.1"]}'). This goes beyond the schema description, though schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('query') and resource ('npm security advisories'), and clarifies it's for a bulk set of packages and versions. It distinguishes itself from sibling npm tools like npm_package or npm_search by focusing on bulk advisory queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for bulk advisory queries with an example, but does not explicitly state when to use this tool versus alternatives like deps_advisory or ghsa_package. No guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get day-by-day npm download counts for a date range. Useful for detecting download anomalies or sudden spikes that may indicate dependency confusion attacks.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name | |
| start | Yes | Start date in YYYY-MM-DD format | |
| end | Yes | End date in YYYY-MM-DD format |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It explains the tool returns day-by-day counts, but lacks details on data sources, rate limits, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states action, second provides a use case. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with three parameters and no output schema, the description is mostly sufficient. It implies the output but could state the return format more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes all three parameters thoroughly (100% coverage). The description adds only minor context (day-by-day nature) beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets day-by-day npm download counts for a date range. However, it does not distinguish from the sibling tool 'npm_downloads', which may have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a specific use case: detecting download anomalies or spikes for dependency confusion attacks. It does not mention when not to use it or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch npm download counts for a package over a given period (last-day, last-week, last-month).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name | |
| period | No | Download count period | last-month |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It does not disclose potential errors (e.g., package not found), authentication needs, rate limits, or response format. The read-only nature is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundant words. It is front-loaded with the core action and includes the key parameter variation in parentheses.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description is adequate. It specifies the input and purpose but does not describe the return value structure. However, the scope is limited enough that this might be acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no new meaning beyond the schema; it restates the period options in parentheses. The schema already includes descriptions and enum for 'period'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and the resource 'npm download counts for a package', and specifies the period options in parentheses. It distinguishes from sibling tools like npm_package or npm_download_range, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching download counts but provides no explicit guidance on when to use this tool versus alternatives like npm_download_range or npm_package. No conditions or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract maintainers and publish timeline from an npm package. Critical for detecting maintainer takeover attacks like the event-stream incident.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what the tool extracts but does not disclose behavioral traits like network access, read-only nature, rate limits, or potential side effects. The extraction action is implied to be read-only, but not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that front-load the core functionality and provide a concrete security example. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description does not specify the return format (e.g., list of maintainers with emails, timeline as dates). While the name and use case hint at the output, a brief note on structure would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'name' has schema description 'npm package name', which is adequate. The tool description does not add further details or constraints. With 100% schema coverage, this meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts maintainers and publish timeline from npm packages, and explicitly ties it to a critical security use case (detecting maintainer takeover attacks like event-stream). This distinguishes it from sibling tools like npm_package or npm_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for security analysis of npm packages, but does not explicitly contrast with alternatives (e.g., npm_package for general info). The use case is clear, but lacks when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch npm package metadata including description, latest version, maintainers, license, repository, homepage, and publish timeline.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name, e.g. 'express' or '@scope/pkg' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry burden. 'Fetch' implies read-only, but no mention of auth, rate limits, or side effects. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single efficient sentence listing key returned fields. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple metadata fetch with one param and no output schema, listing specific fields suffices. Could mention return format or error cases, but not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter 'name' is fully described in schema (100% coverage). Tool description repeats fields but adds no new param-level details beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Fetch' and resource 'npm package metadata', listing concrete fields (description, latest version, etc.). Clearly distinguishes from siblings like npm_search or npm_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives (e.g., npm_version for version data, npm_search for discovery). Lacks context or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether an npm package version has Sigstore provenance attestations and signatures. Key for detecting unauthorized publishes like the Axios-style attacks.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name | |
| version | Yes | Exact semver version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It fails to mention if the tool requires authentication, makes network calls, or has rate limits. The return format is not described, leaving the agent uncertain about what output to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences: one stating the main function and one providing context. Every word is essential, and the information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description covers the primary action but omits details about the return value (e.g., boolean, object) and error handling. This leaves minor gaps for an AI agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for both 'name' and 'version' with 100% coverage. The description adds no additional parameter details beyond the schema, so it meets the baseline but does not enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'check' and the resource 'npm package version Sigstore provenance attestations and signatures'. It also provides context about detecting unauthorized publishes, making its purpose distinct from sibling tools like npm_attestations by focusing on provenance and signatures.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The mention of 'Key for detecting unauthorized publishes like the Axios-style attacks' provides clear context on when to use it. However, it does not explicitly state when not to use it or mention alternative tools, such as npm_attestations for broader attestation checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract and analyze lifecycle scripts from a specific npm package version. Flags suspicious commands (curl, wget, eval, exec, etc.) commonly used in supply-chain attacks.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name | |
| version | Yes | Exact semver version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes script extraction and flagging of suspicious commands, but does not disclose whether scripts are executed, read-only nature, or any other behavioral traits. With no annotations, more detail expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-loaded with purpose. No redundant words, efficient communication of functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with 2 parameters and no output schema; description adequately covers functionality. Could mention return format or error handling, but not critical for a focused analysis tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already describes both parameters (name and version) with clear descriptions; description adds no extra meaning. Baseline 3 justified as schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool extracts and analyzes lifecycle scripts from a specific npm package version, with specific mention of flagging suspicious commands. Distinguishes itself from sibling tools focused on general package info or other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for analyzing lifecycle scripts in supply chain attacks, but no explicit guidance on when to use vs alternatives (e.g., npm_package). Missing when-not conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the npm registry for packages matching a query string.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query text | |
| limit | No | Max number of results (default 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior, but it only states the basic function. No mention of authentication needs, rate limits, result format, or pagination behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no unnecessary words, efficient. However, it sacrifices completeness for brevity; slightly more detail would be acceptable without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with no output schema and low complexity, the description gives the core function but lacks details on expected return data, pagination, or edge cases. Adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for both 'query' and 'limit' parameters. Description adds no additional meaning beyond what the schema already provides, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'search' is specific, the resource 'npm registry' is clear, and it distinguishes from sibling npm tools like npm_package, npm_version, and npm_downloads which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus other npm registry tools or alternative search methods. No when-not-to-use or prerequisites described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch metadata for a specific npm package version including dependencies, dist info (tarball, shasum, integrity), scripts, and deprecation status.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name | |
| version | Yes | Exact semver version, e.g. '4.18.2' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of behavioral disclosure. It implies a read-only operation by describing metadata retrieval, but does not confirm side-effect free or mention authentication, rate limits, or response size. Sufficient for a simple fetch but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that quickly conveys purpose and content. No wasted words, but could be slightly more structured (e.g., list format). Front-loading is adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two simple parameters and no output schema, the description covers the return fields (dependencies, dist info, scripts, deprecation status) well. It is complete enough for a basic read tool, though annotations would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both 'name' and 'version' have descriptions). The tool description does not add further parameter meaning—it only describes what the response contains. Baseline 3 is appropriate as schema already documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool fetches metadata for a specific npm package version and lists included fields (dependencies, dist info, scripts, deprecation status). This differentiates it from sibling tools like npm_package (general package info) and npm_scripts (scripts only), though it doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as npm_package or npm_search. The description does not mention expected input or output context, leaving the agent to infer usage from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get specific version details from NuGet including dependency groups, description, license, and catalog metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | NuGet package name | |
| version | Yes | Package version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies what data is returned (dependency groups, description, etc.) but does not explicitly state it is a read-only operation or mention any required permissions, rate limits, or error behavior. The description adds some value beyond simple naming but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 16 words, concisely stating the tool's purpose and what it includes. It is front-loaded with the key action and resource, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parameter structure (two required strings) and no output schema, the description adequately covers return content (dependency groups, description, etc.). It could be more complete by mentioning the return format or error scenarios, but it is sufficient for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the two parameters with 100% coverage ('NuGet package name' and 'Package version'). The description does not add any additional semantics, such as format constraints or case sensitivity. Given high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('specific version details from NuGet'), clearly indicating it retrieves detailed information for a particular NuGet package version. It lists included fields (dependency groups, description, license, catalog metadata), making it distinct from sibling tools like 'nuget_package' (general info) and 'nuget_versions' (list of versions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives. It lacks explicit context for when to use or not use, and no alternative tools are mentioned. This forces the agent to infer usage from the name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch NuGet package registration metadata including all versions, dependency groups, descriptions, and catalog entries.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | NuGet package name, e.g. 'Newtonsoft.Json' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Fetch' implying a read operation, but does not disclose any behavioral traits such as idempotency, error handling, rate limits, or potential side effects. The description is minimal and does not add value beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no unnecessary words. It is front-loaded with the verb and resource, then lists included details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema or annotations, the description gives a reasonable overview of the output contents (versions, dependencies, descriptions, catalog entries). It could mention the response structure or potential limitations, but for a simple one-parameter tool it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with a clear description and example for the only parameter. The tool description does not add any additional meaning beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch', the resource 'NuGet package registration metadata', and enumerates the included details (all versions, dependency groups, descriptions, catalog entries). This distinguishes it from sibling tools like 'nuget_versions' (likely just versions) and 'nuget_search' (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool compared to alternatives. No explicit 'when to use' or 'when not to use' context, nor mentions of prerequisites or exclusions. The sibling list is not referenced.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the NuGet registry for packages matching a query string.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| limit | No | Max results (default 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It does not disclose rate limits, pagination behavior, authentication needs, or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no redundant information. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and simple parameters, description is minimal. Lacks details on result format, pagination, or error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters with basic descriptions. Description adds no extra meaning beyond 'Search the NuGet registry for packages matching a query string.'
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb (search) and resource (NuGet registry packages). It distinguishes from sibling tools like nuget_package, nuget_versions, etc., which are for specific package details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this vs alternatives (e.g., gem_search, npm_search). No prerequisites or use cases mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all published versions of a NuGet package from the flat container index.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | NuGet package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only mentions listing from a 'flat container index' without explaining behavior such as pagination, rate limits, or error handling. The description does not fully compensate for missing annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no unnecessary words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one parameter and no output schema, the description provides the core functionality but lacks detail on what to expect (e.g., an empty list for invalid packages) and source context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one required parameter 'name' described as 'NuGet package name'. The description adds no additional meaning beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all published versions of a NuGet package from the flat container index. It uses a specific verb ('list') and resource ('versions of a NuGet package'), and is distinguishable from siblings like nuget_package or nuget_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. Siblings like nuget_package might also provide version info, but no distinction is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch full CVE details from NVD (NIST National Vulnerability Database) by CVE ID.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE identifier (e.g. CVE-2023-44487) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose rate limits, authentication requirements, return format, or any behavioral traits beyond 'fetch full details'. Lacks transparency for external API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action and resource. Every word is necessary and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple with one parameter and no output schema. Description covers basic purpose but does not specify return structure or potential errors. Adequate but minimal.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes the parameter. Description does not add new meaning beyond restating 'by CVE ID'. Expected baseline score for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (fetch), resource (full CVE details), source (NVD), and input method (by CVE ID). Distinguishes from siblings like nvd_search and nvd_recent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied use case (have a CVE ID, fetch its details). No explicit when-to-use, when-not-to-use, or alternative tools mentioned. Sibling tools like nvd_search or nvd_recent are not referenced.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch recently published CVEs from NVD within a given number of days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days back to search (default 7) | |
| results_per_page | No | Number of results to return (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It correctly indicates a read operation ('Fetch') but does not expand on potential behavioral traits like rate limits, pagination behavior, or response structure. The description is accurate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence front-loaded with the action and resource. Every word contributes meaning, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 optional parameters, read-only operation), the description covers the core action and scope. Without an output schema, more detail on return format could help, but the defaults and parameter descriptions provide adequate context for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters ('days' and 'results_per_page') having descriptions including defaults. The tool description adds no additional semantic information beyond what is already in the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Fetch', the resource 'recently published CVEs', and the scope 'within a given number of days'. It distinguishes from sibling tools like nvd_search (keyword search) and nvd_cve (specific CVE by ID) by focusing on recency and timeframe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching recent CVEs by number of days. It does not explicitly state when not to use or list alternatives, but the specificity of 'recently published' provides clear context that this is for time-based retrieval rather than search by ID or keyword.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search NVD for CVEs by keyword and optional CVSS v3 severity.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Search keyword (e.g. 'log4j', 'xss nginx') | |
| severity | No | CVSS v3 severity filter (LOW, MEDIUM, HIGH, CRITICAL) | |
| results_per_page | No | Number of results to return (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description fails to disclose behavioral traits like pagination, rate limits, authentication, or handling of no results. Only states the basic search functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with purpose, no unnecessary words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity (3 params, no output schema), the description lacks details on return format, pagination, or search behavior. An agent needs more context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. Description adds no extra meaning beyond repeating parameter existence; severity filter is named but not elaborated beyond the schema's enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly specifies verb 'search', resource 'NVD for CVEs', and key parameters 'keyword' and 'optional CVSS v3 severity'. It distinguishes from sibling tools like nvd_cve (specific CVE) and nvd_recent (recent vulnerabilities).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives (e.g., nvd_cve, nvd_recent). Missing context about use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Batch query OSV database for vulnerabilities across multiple packages at once.
| Name | Required | Description | Default |
|---|---|---|---|
| queries | Yes | Array of package queries to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose behavioral traits such as rate limits, authentication needs, or error handling for partial failures.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and to the point, but may be too terse given the lack of annotations and output schema. It conveys the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema or annotations, the description provides minimal context. It does not explain return values or error scenarios, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes parameters adequately. The description adds no additional meaning beyond naming the action.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a batch query for vulnerabilities across multiple packages. However, it does not explicitly differentiate from sibling tools like osv_query, which may be for single queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for multiple packages via 'batch', but lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch full vulnerability details from OSV by ID (OSV, CVE, GHSA, RUSTSEC, PYSEC, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Vulnerability ID (e.g. GHSA-xxxx-xxxx-xxxx, CVE-2023-1234, RUSTSEC-2023-0001) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only says 'Fetch full vulnerability details'—it does not disclose read-only nature, error handling (e.g., missing ID), or any behavioral traits beyond the basic action. Acceptable for a simple fetch but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, concise sentence that front-loads the action and resource, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter, no-output-schema tool, the description fully covers what the tool does and what input is needed, leaving no significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the single parameter with a full description of acceptable formats; the tool description adds only 'etc.' for other ID types, providing marginal extra value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Fetch' and resource 'full vulnerability details from OSV' and explicitly lists accepted ID formats (OSV, CVE, GHSA, etc.), clearly distinguishing it from sibling tools that query differently.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when you have a specific ID, but provides no explicit guidance on when not to use it or alternatives like osv_batch or osv_query, leaving the agent to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query OSV database for known vulnerabilities affecting a specific package and optional version.
| Name | Required | Description | Default |
|---|---|---|---|
| ecosystem | Yes | Package ecosystem (e.g. npm, PyPI, Go, crates.io, Maven) | |
| name | Yes | Package name | |
| version | No | Exact package version to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It indicates a read operation (non-destructive) but lacks details on rate limits, authentication, error handling, or response format. The clarity that it queries vulnerabilities is minimal but adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, straightforward sentence with no unnecessary words. It front-loads the action and resource, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description omits important context like return format (list of vulnerability IDs/details) and the optional nature of the version parameter. Given no output schema, the agent lacks crucial information to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The tool description does not add extra meaning beyond the schema, such as version format or default behavior when version is omitted. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries the OSV database for vulnerabilities, specifying action and resource. It distinguishes from sibling tools by focusing on package and optional version, differentiating from osv_query_commit and osv_query_purl.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like osv_id or osv_batch. It does not mention prerequisites, limitations, or scenarios where other tools would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query OSV database for vulnerabilities associated with a specific git commit hash.
| Name | Required | Description | Default |
|---|---|---|---|
| commit | Yes | Git commit hash to query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only states that it queries vulnerabilities; no disclosure of error handling, rate limits, or what happens if commit is not found. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, clear and direct. No extraneous words, front-loaded with the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple tool with one parameter, but lacks information about return format or edge cases. Since no output schema exists, description could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% as the only parameter 'commit' has a description. The description adds 'git commit hash' which marginally improves clarity but adds little beyond schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Query', resource 'OSV database', and scope 'vulnerabilities associated with a specific git commit hash'. It distinguishes from sibling tools like osv_id, osv_query_purl, and osv_batch by specifying the commit hash input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus siblings (e.g., osv_query, osv_query_purl). No mention of prerequisites or context, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query OSV database using a Package URL (purl) for known vulnerabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| purl | Yes | Package URL (e.g. pkg:npm/express@4.17.1) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey all behavioral traits. It only states the basic action without mentioning what happens on success/failure, rate limits, or any side effects. For a query tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that is clear and free of redundancy. It is effectively front-loaded with the key action and input.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description is adequate but lacks information about return values, error handling, or when to prefer this over similar tools. It is missing contextual details that would help an agent decide.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the purl parameter (100% coverage). The tool description adds no additional meaning beyond the schema, so it meets but does not exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Query'), the data source ('OSV database'), the input method ('using a Package URL (purl)'), and the output purpose ('for known vulnerabilities'). It distinguishes itself from siblings like osv_query and osv_query_commit by specifying the purl input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (when you have a purl), but it does not provide explicit guidance on when not to use it or mention alternatives. For example, it doesn't compare to osv_query or osv_batch.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract author and maintainer information from a PyPI package. Useful for detecting ownership changes or suspicious maintainer patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | PyPI package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states the tool extracts information but does not mention side effects, permissions, rate limits, or that it is a read-only operation. This omission is significant for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long with no fluff. The action ('Extract') is front-loaded, and every sentence provides value. It is an example of efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is largely complete. It explains what the tool returns (author/maintainer info) and common use cases. However, it could briefly mention the return format or typical fields to be fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter 'name', with a clear description 'PyPI package name'. The tool description does not add extra semantic details beyond the schema, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Extract' and the resource 'author and maintainer information from a PyPI package'. It distinguishes from sibling tools like pypi_package by focusing specifically on maintainer data, though it could be more precise about what information is extracted (e.g., emails, names).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage context: 'useful for detecting ownership changes or suspicious maintainer patterns'. However, it does not explicitly state when not to use this tool (e.g., if only package metadata is needed) or mention alternatives like pypi_package. The guidance is clear but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch PyPI package metadata including author, license, summary, project URLs, classifiers, and Python version requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | PyPI package name, e.g. 'requests' or 'django' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the full burden. It states the tool fetches metadata but does not disclose whether it is read-only, any rate limits, authentication needs, or whether the data might be cached. The description is minimal and lacks behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the verb and resource. It is concise, front-loaded, and contains no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter, no output schema, and no annotations, the description adequately covers the tool's purpose and expected outputs. It is complete for a simple metadata fetch operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter ('name') with a descriptive example. Since schema coverage is 100%, the description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches PyPI package metadata and lists specific data fields (author, license, summary, etc.). However, it does not differentiate from sibling tools such as pypi_version or pypi_maintainers, which also retrieve package-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like pypi_version or pypi_releases. The description offers no context-specific usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all releases of a PyPI package with upload dates, sizes, and yanked status. Useful for detecting suspicious rapid version bumps or yanked releases.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | PyPI package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as rate limits, authentication requirements, or side effects. It only states the listing function without any safety or behavior context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first clearly states functionality, second gives a practical use case. No unnecessary words, front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and no output schema, the description adequately covers the tool's purpose and output fields. However, it could be more complete by mentioning pagination or the full response format, but it is sufficient for this simple case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'name' is fully described in the input schema (100% coverage). The description does not add any additional semantics beyond what the schema provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list releases), the resource (PyPI package), and the output fields (upload dates, sizes, yanked status). It differentiates from siblings like pypi_package and pypi_version by focusing specifically on releases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a use case ('detecting suspicious rapid version bumps or yanked releases'), which guides the agent on when to use this tool. However, it does not explicitly state when not to use it or mention alternative siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch metadata for a specific PyPI package version including release URLs with upload times, file sizes, digests, and yanked status.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | PyPI package name | |
| version | Yes | Exact version string, e.g. '2.31.0' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation by stating it fetches metadata, but it does not explicitly declare it as non-destructive or disclose potential errors (e.g., package not found) or API behavior. With no annotations, the description is minimally adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently communicates the tool's functionality, front-loading the main action and detailing included metadata with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately states the input and data included, but does not describe the output structure or format. Given no output schema, the description could be more complete by hinting at the response shape, but for a simple tool it remains sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes both parameters with clear descriptions and an example for version. The tool description does not add any additional parameter semantics beyond confirming the tool's purpose. With 100% schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'Fetch' with the resource 'metadata for a specific PyPI package version' and enumerates included data (release URLs, upload times, file sizes, digests, yanked status). This clearly differentiates from sibling tools like pypi_package (package overview) and pypi_releases (list versions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as pypi_package or pypi_releases. It does not state when not to use or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve multiple Rekor log entries by their UUIDs or log indexes in a single request
| Name | Required | Description | Default |
|---|---|---|---|
| entry_uuids | No | Array of entry UUIDs to retrieve | |
| log_indexes | No | Array of log indexes to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only says 'retrieve', failing to disclose behavior like error handling, rate limits, or side effects. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no extraneous words. Efficiently conveys the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without output schema or annotations, the description lacks information on return format, partial failures, or pagination. Incomplete for a batch retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and description adds no additional meaning beyond the schema descriptions. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves multiple Rekor log entries by UUIDs or log indexes in a single request, distinguishing it from sibling tools like rekor_entry (single entry) or rekor_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for batch retrieval but lacks explicit when-to-use, when-not-to-use, or comparison with siblings like rekor_entry for single entries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a specific Rekor transparency log entry by UUID, including body, attestation, and inclusion proof
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | Rekor entry UUID (64 or 80 hex character string) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool returns entry details, but lacks info on authentication, rate limits, or safety (though retrieve is inherently read-only). The description is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single well-structured sentence that efficiently conveys the action, input, and output. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool (one parameter, no output schema), the description covers the main aspects. It could mention if any return format or limits apply, but it's sufficient for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single required parameter 'uuid'. The description adds specificity: '64 or 80 hex character string', which is not in the schema. This gives the agent clearer format expectations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a specific Rekor transparency log entry by UUID and lists the returned components (body, attestation, inclusion proof). This distinguishes it from sibling tools like rekor_search (which searches) and rekor_verify (which verifies). The verb 'retrieve' and resource 'Rekor transparency log entry' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a specific UUID, but it does not explicitly guide when to use this tool versus siblings like rekor_entries_search or rekor_search. No exclusions or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current Rekor transparency log status including rootHash, treeSize, signedTreeHead, and treeID
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It does not disclose whether the operation is read-only, requires authentication, or has any side effects. Although 'get' implies a read, the description misses an opportunity to explicitly state behavioral traits like safety or latency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, efficiently states purpose and returned fields. No extraneous words. Front-loads the action and result.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless read tool, the description is sufficient: it clarifies the purpose and output fields. However, it could briefly explain the significance of the returned fields (e.g., 'the latest signed tree head') or mention that it returns a JSON object. Given no output schema, slight additional context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100% (trivially). The description adds value by listing the returned fields (rootHash, treeSize, signedTreeHead, treeID), which are not documented in the input schema. This helps the agent understand what information the tool provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('Rekor transparency log status'), and lists the exact fields returned (rootHash, treeSize, signedTreeHead, treeID). It clearly distinguishes from sibling tools like rekor_entry or rekor_search which deal with individual entries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. However, the purpose and zero parameters imply it's for global log status, contrasting with entry-specific sibling tools. The description could mention it's for obtaining the current log checkpoint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Rekor transparency log by email, SHA256 hash, or public key fingerprint and return matching entry UUIDs
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query: an email address, SHA256 hash (with sha256: prefix), or public key fingerprint |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the search types and that the output is UUIDs, which is appropriate for a read-only search operation. It could mention that no side effects occur, but the description is sufficiently transparent for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence with no unnecessary words. It is front-loaded with the verb and resource, and each part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with one parameter and no output schema, the description covers the essential aspects: purpose, query types, and return value. It could briefly mention what happens on no results or multiple results, but it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a generic description for the 'query' parameter. The description adds specific meaning by explaining the valid formats (email, SHA256 with prefix, fingerprint), which significantly enhances the schema's bare description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'Rekor transparency log', and the specific search types (email, SHA256 hash, public key fingerprint), and that it returns entry UUIDs. This differentiates it from sibling tools that search other registries or package ecosystems.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description explains what it searches by, it does not explicitly state when to use this tool versus alternatives, nor does it provide any conditional guidance or when-not-to-use instructions. The context of sibling tools implies it's for Rekor-specific searches, but more explicit usage guidance would improve score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify whether a SHA256 artifact hash has been recorded in the Rekor transparency log, returning entry details if found
| Name | Required | Description | Default |
|---|---|---|---|
| hash | Yes | SHA256 hash of the artifact to verify, with or without "sha256:" prefix |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly describes the operation (verification, returning entry details if found) and implies read-only behavior. However, it could explicitly state that it is non-destructive or read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It front-loads the purpose and is appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately covers the input (hash) and output (entry details if found). It is complete enough, though it could briefly mention that no result indicates the hash is not recorded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema description for the 'hash' parameter already provides the same detail as the tool description (SHA256 hash with optional 'sha256:' prefix). The description does not add additional parameter semantics beyond what the schema already provides, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Verify', the resource 'SHA256 artifact hash', and the system 'Rekor transparency log', with the outcome 'returning entry details if found'. It distinguishes this tool from siblings like rekor_entry (get specific entry) and rekor_search (search by criteria).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for verifying a hash presence, but it does not explicitly state when not to use this tool or mention alternatives (e.g., for broader searches). The context is clear but lacks exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find packaging problems/issues for a repository on Repology.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository name, e.g. 'ubuntu_24' or 'fedora_40' or 'homebrew' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description fails to disclose any behavioral traits such as read-only nature, output format, rate limits, or what constitutes 'problems'. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with one sentence and no wasted words. However, it is too sparse and lacks necessary context, which reduces effectiveness despite brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is inadequate. It does not explain the return format, pagination, or what constitutes problems/issues, leaving the agent with insufficient information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the only parameter 'repo' has a description with examples). The tool description does not add any further semantic information beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'Find' and resource 'packaging problems/issues' for a repository on Repology. It distinguishes from sibling tools like repology_project and repology_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. The description implies usage for finding problems but does not provide when-not-to-use or mention other repology tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get package versions across all Linux distributions from Repology.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Project name, e.g. 'nginx' or 'python' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It does not mention rate limits, data freshness, or if only latest versions are returned. The description lacks depth beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and no output schema, the description is adequate but lacks information about return format, pagination, or potential errors. It is minimally complete for a simple lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the parameter 'name' is described with examples. However, the description adds no additional meaning beyond the schema, which already explains the purpose. Baseline score is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'package versions' with scope 'across all Linux distributions'. It effectively distinguishes from sibling tools like repology_search which is for searching projects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. The tool is straightforward (one required parameter), but there is no mention of alternatives like repology_search for broader searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Repology projects by name.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query for project name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description bears full responsibility for behavioral disclosure. It provides no information about limits, pagination, error behavior, or side effects. The single sentence gives no insight into tool behavior beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise but under-specified. While no words are wasted, it omits crucial details that would make the tool usable, such as output format or behavioral notes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing output schema and any description of return values. For a search tool, key context like result format, pagination, or field support is absent, making the description inadequate for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter having a clear description. The tool description repeats 'by name' which aligns with the schema description. It does not add new semantic meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the primary action (search) and resource (Repology projects) with a condition (by name). It is clear but essentially restates the tool name and the parameter description, adding little new meaning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus sibling tools like repology_project or repology_problems. The description lacks context for appropriate usage or exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare OpenSSF Scorecard security scores across 2-5 GitHub repositories side by side
| Name | Required | Description | Default |
|---|---|---|---|
| repos | Yes | Array of {owner, repo} objects to compare (2-5 repos) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides no behavioral details beyond purpose. It does not mention output format, error handling, rate limits, or authentication requirements, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 10 words directly states the core functionality with no redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only one parameter, no output schema, and no annotations, the description is too minimal. It omits what 'side by side' means in output terms and assumes domain knowledge of OpenSSF Scorecard.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and fully describes the 'repos' parameter. The description echoes constraints but adds no new meaning or context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb 'compare', the specific resource 'OpenSSF Scorecard security scores', and the constraint of 2-5 GitHub repositories, distinguishing it from sibling 'scorecard_repo' for single repos.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly suggests usage for multiple repos by stating '2-5 GitHub repositories', but lacks explicit when-to-use or alternatives. Sibling tools include 'scorecard_repo', but no direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the OpenSSF Scorecard security score for a GitHub repository, including individual check results
| Name | Required | Description | Default |
|---|---|---|---|
| owner | Yes | GitHub repository owner | |
| repo | Yes | GitHub repository name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a read operation but doesn't disclose specific behaviors such as rate limits, authentication needs, or data freshness. It is adequate but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no unnecessary words. Efficiently conveys the tool's action and return value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (2 params, no output schema), the description covers the main purpose. It mentions both the score and individual results, but lacks specifics on return format or value range.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage for both parameters. The description does not add meaning beyond what the schema already provides (owner and repo are self-explanatory).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'OpenSSF Scorecard security score', and includes that individual check results are returned. It distinguishes from sibling 'scorecard_compare' by focusing on a single repository.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'scorecard_compare'. No context about prerequisites or typical scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all 21 supply chain security data sources with their configuration status, required environment variables, available tools, and API base URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adequately states it lists sources and returns configuration status, env vars, tools, and API URLs. No side effects or additional behavioral context are provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with zero wasted words. Efficiently communicates the action and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is complete in stating what the tool does and what it returns. Could be improved by noting it's a list of endpoints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. The description adds value by specifying the output fields beyond the schema, though schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List all 21 supply chain security data sources' with specific details returned. This distinguishes it from sibling tools that query individual sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies using this tool to get an overview, but lacks explicit guidance on when to use versus alternatives or when not to use. No exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if a package name is suspiciously similar to popular packages (potential typosquatting). Returns matches with edit distance <= 2.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name to check for typosquatting | |
| ecosystem | No | Package ecosystem: "npm" or "pypi" (default: "npm") | npm |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the key behavioral trait: returns matches with edit distance <= 2. Since no annotations exist, this adds important context. However, it does not detail what happens with no matches or how popular packages are determined.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, highly concise, and front-loaded with the main action. Every phrase adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description covers the core behavior and threshold. Minor gaps exist about the reference list of popular packages, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context about 'popular packages' but does not enrich parameter meaning beyond the schema's descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: checking if a package name is similar to popular packages for typosquatting, with a specific edit distance threshold. It distinguishes from sibling 'typosquat_compare' by focusing on similarity detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for typosquatting detection but does not explicitly guide when to use this tool versus alternatives like 'typosquat_compare'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two package names directly to assess typosquatting risk, showing edit distance, similarity percentage, character-level diff, and confusable character warnings
| Name | Required | Description | Default |
|---|---|---|---|
| name_a | Yes | First package name | |
| name_b | Yes | Second package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It describes what the tool shows but does not mention side effects, authentication needs, rate limits, or whether it is read-only. The description is adequate but incomplete for a non-annotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the main action. It could benefit from slight restructuring for readability, but it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 simple params, no output schema), the description sufficiently explains the tool's purpose and outputs. It could mention the output format or return value, but it is mostly complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description only restates that they are package names, adding no extra meaning beyond the schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool compares two package names for typosquatting risk and lists specific outputs (edit distance, similarity, diff, confusable warnings). It distinguishes from sibling typosquat_check, which likely checks a single name against known patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when comparing two specific names, but does not explicitly guide when to use vs. typosquat_check or other tools. No when-not-to-use or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0badge_by_repobadge_projectbadge_searchcomposer_advisoriescomposer_packagecomposer_searchcomposer_statscrate_depscrate_infocrate_ownerscrate_versionsdeps_advisorydeps_dependenciesdeps_dependentsdeps_packagedeps_projectdeps_purl_lookupdeps_querydeps_requirementsdeps_similar_packagesdeps_versionepss_above_thresholdepss_batchepss_scoreepss_topgem_infogem_reverse_depsgem_searchgem_versionsghsa_idghsa_packageghsa_recentghsa_searchgo_modulego_sumgo_versiongo_vuln_by_modulego_vuln_db_infogo_vuln_idgo_vuln_listkev_lookupkev_recentkev_searchkev_statslibraries_dependentslibraries_depslibraries_packagelibraries_sourceranklicense_batchlicense_lookuplicense_searchnpm_attestationsnpm_audit_bulknpm_download_rangenpm_downloadsnpm_maintainersnpm_packagenpm_provenancenpm_scriptsnpm_searchnpm_versionnuget_catalog_entrynuget_packagenuget_searchnuget_versionsnvd_cvenvd_recentnvd_searchosv_batchosv_idosv_queryosv_query_commitosv_query_purlpypi_maintainerspypi_packagepypi_releasespypi_versionrekor_entries_searchrekor_entryrekor_log_inforekor_searchrekor_verifyrepology_problemsrepology_projectrepology_searchscorecard_comparescorecard_reposupplychain_list_sourcestyposquat_checktyposquat_compareScored across 90 tools
Most tools are clearly separated by ecosystem prefix and specific purpose, so an agent can usually tell them apart. There are a few boundary overlaps, such as npm_package vs npm_maintainers and npm_provenance vs npm_attestations, which could cause occasional misselection.
Tool names follow a consistent ecosystem_prefix + noun pattern, with mostly snake_case throughout. Minor inconsistencies exist in verb choice (search/lookup/query/fetch) and a few odd names like epss_above_threshold and rekor_entries_search, but overall the pattern is predictable.
With 90 tools, this server is far beyond the recommended scope for effective agent coherence. Even though the domain is broad, this many tools creates significant navigation and selection overhead for an agent.
The server covers an impressively wide range of supply-chain security data: package registries, vulnerability databases, dependency graphs, provenance, licenses, scorecards, and transparency logs. Some gaps exist, such as no direct Maven/Java support and no dedicated PyPI or crates.io search, but the core supply-chain investigation workflows are well covered.
Package intelligence MCP for AI agents — 22 tools, 19 ecosystems, AGPL SDK, free.
AI-security knowledge as MCP: standards-mapped tools (OWASP, NIST, MITRE) for AI agents.
Zero-config MCP security scanner for AI-generated apps. 25K+ vulnerability patterns.
One MCP server over 253 data sources: Reddit, LinkedIn, YouTube, G2, search and AI answers.
