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exploitwatch

check_dependencies_against_kev

Check a comma-separated list of software dependency/product names against CISA's live Known Exploited Vulnerabilities (KEV) catalog. Returns any matches with the CVE, vulnerability name, and why it matched. Call this before shipping or during a dependency/security review to catch actively-exploited components.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dependenciesYesComma-separated dependency/product names, e.g. "express, lodash, openssl"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does reasonably well: it discloses the data source is a 'live' external catalog and states what matching produces (CVE, vulnerability name, match reason), which implies an empty result when nothing matches. It omits details like latency, rate limits, or match fuzziness (exact vs. partial/version-aware), keeping it short of a 5.

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?

Two sentences, zero filler, front-loaded with the action and resource before the usage context. Every clause earns its place.

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

Completeness4/5

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

For a single-parameter lookup with no output schema, the description usefully summarizes the return payload (CVE, vulnerability name, match reason) and when to run it. It could add a bit on match behavior and error/no-match handling, but nothing essential is missing to invoke the tool.

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

Parameters3/5

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

Schema coverage is 100% and the single parameter is fully documented in the schema itself, so baseline is 3. The description only restates 'comma-separated list' without adding matching semantics such as case sensitivity, exact vs. partial name matching, or whether versions are accepted.

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 names a specific verb (check) and resource (software dependency/product names) against a named external source (CISA's live KEV catalog). An agent immediately knows what the tool does and what data it consults.

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

Usage Guidelines4/5

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

It gives explicit triggers: 'Call this before shipping or during a dependency/security review to catch actively-exploited components.' No alternatives are named, but with no sibling tools there is nothing to disambiguate against, so the context is sufficient.

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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