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security_fetch_package_vulnerabilities

Read-onlyIdempotent

Fetch all known CVEs for an open source package version or a batch of packages. Read-only. No side effects. Idempotent. Single-package mode: package (e.g. requests), version (e.g. 2.28.0), ecosystem (PyPI/npm/Maven/Go/Cargo/NuGet/RubyGems). Batch mode: packages array of {name, version, ecosystem} objects — max 50 per call. If packages array is provided and non-empty, batch mode is used and package/version/ecosystem are ignored. Batch returns {results: [...], partial: bool, failed_count: int}. Each result has vuln_count and vulnerabilities list. Returns CVE ID, severity, CVSS score, affected range, and fixed version. Use security_fetch_cve_detail for full detail by CVE ID. Use security_audit_sbom_vulnerabilities for SBOM files. Verified source: Google OSV.dev. 1-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="security_fetch_package_vulnerabilities", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageNoPackage name e.g. requests. Required in single-package mode.
versionNoPackage version e.g. 2.28.0. Required in single-package mode.
packagesNoBatch list of {name, version, ecosystem} objects. Max 50.
ecosystemNoPackage ecosystem: npm, pypi, cargo, go, maven, nuget. Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds substantial behavioral context: it explicitly states 'Read-only. No side effects. Idempotent.', mentions a 1-hour cache, identifies Google OSV.dev as the source, and explains batch response semantics (partial flag, failed_count). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact but information-dense. The opening sentence states the core purpose, followed by structured mode explanations. Every sentence adds value—from mode-specific behavior to return format to fallback instructions. No wasted words or tautology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (dual modes, batch limits, multiple return fields) and the presence of an output schema, the description fully covers what an agent needs: mode selection, parameter semantics, result shape, and error fallback. It is self-sufficient and leaves no critical gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema description coverage is 100%, the description adds crucial semantic detail: it explains the relationship between single-package fields and the packages array, clarifies which fields are ignored in batch mode, and describes the batch object structure ({name, version, ecosystem}). This goes well beyond the schema's individual field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a specific verb and resource: 'Fetch all known CVEs for an open source package version or a batch of packages.' It clearly distinguishes between single-package and batch modes, and differentiates from sibling tools like security_fetch_cve_detail and security_audit_sbom_vulnerabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: single-package mode vs batch mode, the 50-item batch limit, and the fact that batch mode overrides single parameters. It also names specific alternatives for related needs (security_fetch_cve_detail, security_audit_sbom_vulnerabilities) and even includes a fallback instruction to call report_feedback if the tool doesn't satisfy the user's need.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation4/5

Tools are grouped into clear domain prefixes (compliance, domain, frontend_security, etc.) with distinct purposes. Minor overlap exists between frontend_security_detect_typosquatting and security_detect_typosquatting, but descriptions clarify the different scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern with snake_case. Irregularities like 'fetch' vs 'audit' and two 'detect_typosquatting' tools exist, but overall naming is predictable within domains.

Tool Count3/5

55 tools is high for a single server given the breadth of domains. Some redundancy (e.g., two typosquatting tools) suggests possible trimming, but the count is justified by the wide coverage.

Completeness4/5

The tool surface covers key operations across domains like compliance, domain, security, legal, and nonprofit. Minor gaps exist, such as limited frontend audit beyond package.json and no general-purpose code scanning.

Resources