Free deterministic security scan of public git repos: OSV.dev vulnerable deps, secrets, config lint.
Scored across 2 tools
The two tools have completely distinct purposes: audit_pricing returns business/pricing information for a paid service, while scan_repository performs an actual security scan on a public git repository. There is no overlap in action, resource, or output, so an agent can easily tell them apart.
Both names follow the same snake_case verb_noun pattern: audit_pricing and scan_repository. The convention is consistent throughout the set.
With only two tools, the surface feels thin for a security scanning server. One tool is purely informational/marketing (audit_pricing), leaving just a single functional operation, which is borderline for the stated purpose.
scan_repository covers a broad range of checks (dependency advisories, secret patterns, config lint) and returns a detailed report, so the core free-scan workflow is well covered. Minor gaps exist, such as no way to retrieve past reports or scan private repositories, but these are reasonable limitations for a free public scanner.
Describe Project Feldspar's paid code audit (security, correctness, maintainability; three independent AI review passes), its price, delivery window, the Terms and the checkout URLs. No arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and a closed world, so the safety profile is fully covered. The description adds useful content context (what information is returned: price, delivery window, Terms, checkout URLs) but discloses nothing about caching, freshness of pricing, or auth/ordering prerequisites.
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 tightly packed sentence front-loads the verb and resource, then enumerates the payload. No filler, no repetition of annotation-provided facts, and the "No arguments" note is placed last where it matters.
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 compensates by enumerating the returned content (scope, price, delivery window, Terms, checkout URLs). For a zero-argument informational tool whose annotations carry the safety profile, this is close to complete; only freshness/currency of pricing info is left unstated.
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 takes zero parameters, which is the baseline-4 case. The description explicitly confirms "No arguments," matching the empty schema with additionalProperties=false and removing any doubt about whether input is expected.
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?
States a specific verb ("Describe") and a precise resource: the paid code-audit offering, including scope, price, delivery window, Terms and checkout URLs. The resource is clearly distinct from the sibling scan_repository (which acts on a repository), though the description never names or contrasts that sibling 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?
Usage is only implied by the content list and the phrase "how to order" in the title — an agent can infer it is an informational pre-purchase lookup. There is no explicit statement of when to call it versus scan_repository, and no exclusions or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Clone a public git repository and run feldspar-scan: OSV.dev advisories for pinned dependencies in lockfiles (npm, pnpm, yarn, pip/uv/poetry, Cargo, Go, Gemfile.lock, composer), secret patterns with redacted evidence, and configuration lint. Returns a JSON report with summary counts and per-finding severity, file, line, advisory id and fixed versions. Deterministic, no LLM involved. Takes 2-90 s depending on repository size.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | https://github.com/owner/repo (also gitlab.com, codeberg.org, bitbucket.org) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/hint annotations, the description discloses useful behavioral traits: it clones the repository, takes 2-90s, is deterministic with no LLM involvement, and redacts secret evidence. This gives an agent realistic expectations for side effects, latency, and output handling.
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?
Four tight sentences front-load the core action, then pack the scan categories, return format, execution guarantees, and latency without redundancy. Every sentence 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 no output schema, the description appropriately explains the JSON report's structure and key finding fields. Combined with the URL format, lockfile support, and latency range, an agent has enough to invoke and interpret the result 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?
There is only one parameter and the input schema already describes the URL format and supported hosts at 100% coverage, so the baseline applies. The description adds no additional parameter-specific meaning beyond echoing public repository scope.
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?
States a specific verb phrase: 'Clone a public git repository and run feldspar-scan', and enumerates the three scan categories plus return format. This clearly differentiates scan_repository from the only sibling audit_pricing, which is about pricing.
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 clearly scopes usage to public git repositories and lists supported hosts, while the deterministic/no-LLM note helps an agent decide when this scan is appropriate. It does not explicitly name a sibling alternative, but the sibling is unrelated (audit_pricing), so the intended context is evident.
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.
audit_pricingscan_repositoryRead-only scan of a public GitHub repo for leaked secrets, with a 0-100 grade.
Generate SBOMs, scan vulnerabilities, and analyze dependencies from local projects or Git repos.
Audit GitHub repos for malicious and supply-chain code before you depend on them.
Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.
Add one secure layer between your agents and this server.
