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fairquery-agent-trust-mcp

Official

Audit dependencies/manifests with fix recommendations (paid)

osv_audit_dependencies
Read-onlyIdempotent

Audit full project dependencies from manifests or exact package lists against OSV; returns per-package severity, CVSS, fixed versions, and recommended upgrades for CI-style reports.

Instructions

WHAT: Runs the Fairquery Apify Actor osv-dependency-vulnerability-check: parses package.json, package-lock.json (v1-v3), requirements.txt or go.mod (or a package list), checks OSV, and returns per-package max severity/CVSS, fixed versions, a recommended upgrade version and one row per vulnerability (deduped aliases). WHEN: Full project audits from a pasted manifest, CI-style reports, severity-filtered output. WHEN NOT: Quick checks of a few exact versions (osv_query_packages, free) or one advisory (osv_get_vulnerability, free). COST: Paid, billed to YOUR Apify account via your APIFY_TOKEN: $0.0009 per package + $0.0028 per vulnerability found (+ $0.00005/run). Use minSeverity="HIGH" to cut vuln rows. lodash+django (2 pkgs, 41 vulns) ~ $0.12. RETURNS: Paged rows type="package" (vulnerable, vuln_count, max_severity, fixed_versions, recommended_version), type="vulnerability" (id, cve_ids, severity, cvss_score, fixed_versions, osv_url) or type="error". Tool errors: INPUT_EMPTY, APIFY_TOKEN_MISSING, APIFY_TOKEN_INVALID, APIFY_PAYMENT_REQUIRED, APIFY_ACTOR_RUN_FAILED, APIFY_TIMEOUT. EXAMPLE: {"manifestText":"django==3.2.0\nrequests==2.19.0","minSeverity":"HIGH"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size: items to return, integer 1-1000 (default 100)
offsetNoItems to skip for paging, integer >= 0 (default 0); pass nextOffset from the previous response
packagesNoUp to 1000 exact versions. Optional if manifestText given
minSeverityNoOnly emit vulns at/above this severity (default ALL)ALL
manifestTextNoRaw package.json / package-lock.json / requirements.txt / go.mod content (<= 2,000,000 chars)
manifestTypeNoManifest format (default auto-detect)auto
includeDevDependenciesNoInclude dev dependencies (default true)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaNoTool-specific context (summary counts, cost notes, ...)
countYesNumber of items in this page
itemsYesResult rows for this page
totalYesTotal items before paging
offsetYesOffset of the first item in this page
hasMoreYesTrue if more items exist after this page
nextOffsetYesOffset to pass for the next page, or null

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations cover only the safety profile (readOnly, idempotent, openWorld, non-destructive); the description goes well beyond by disclosing the billing model ($0.0009/package + $0.0028/vulnerability + per-run fee, with a worked lodash+django example), the required APIFY_TOKEN and account-charging behavior, the paged row types, and the full enumerated error taxonomy. That is exactly the additional context an agent needs before committing to a paid call.

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 label-prefixed structure (WHAT / WHEN / WHEN NOT / COST / RETURNS / EXAMPLE) front-loads the identifying information and makes the routing and cost facts scannable. Despite its length, every section is functional — there is no filler or restatement of the tool name.

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?

For a 7-parameter, paid, open-world tool with 0 required parameters, the description covers inputs, cost, auth prerequisites, output row types, paging, and failure modes. Because an output schema exists it is not obligated to describe returns, yet the summary of row types still aids invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3, but the description adds real decision value: it tells the agent to set minSeverity="HIGH" specifically to cut vulnerability rows and therefore cost, and the EXAMPLE shows manifestText combined with minSeverity in practice. It does not, however, clarify manifestText-vs-packages precedence or the dev-dependency default beyond what the schema already states.

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 WHAT block names a specific verb (runs an audit), the exact resource (OSV database against a pasted manifest or package list), and the concrete output shape (per-package severity/CVSS, fixed versions, one row per vuln). It explicitly differentiates itself from the two sibling tools osv_query_packages and osv_get_vulnerability, so an agent can route without opening any schema.

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?

It gives explicit WHEN (full project audits from a pasted manifest, CI-style reports, severity-filtered output) and WHEN NOT (quick checks of a few exact versions via osv_query_packages, or a single advisory via osv_get_vulnerability), naming both alternatives by name. The condition that selects each path is fully spelled out, leaving nothing to inference.

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