Noaa Coastwatch

Scan Dependency

scan_dependency

Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "package": "left-pad"
    },
    "name": "scan_dependency"
  }
}

Input Schema

Name	Required	Description	Default
`package`	Yes	npm package name. Scoped packages (e.g. "@types/node") are accepted.
`version`	No	Specific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 26, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context beyond annotations: it explains partial failure handling, the 5-30s first-measurement latency on bundlephobia, and that sources_failed will list timeouts while other data still returns. No contradictions 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 dense but efficient, opening with the core composite purpose, then listing return fields, then noting ecosystem scope and failure behavior. Every sentence earns its place; no fluff or redundancy, and the most critical usage guidance is front-loaded.

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?

With no output schema, the description thoroughly enumerates the return structure (summary block fields, per-advisory detail, links, alternative versions) and error behavior. It also covers latency expectations and scope limitations, leaving no gap for an agent to call it correctly.

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 description coverage is 100%, so the schema already documents both 'package' and 'version' including defaults and scoped package acceptance. The description does not add any additional parameter semantics beyond what the schema provides; the baseline of 3 applies since the schema carries the full burden.

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 clearly states the tool's purpose: a composite check for 'should I add this npm package' covering license, advisories, version history, and bundle size via deps.dev and bundlephobia. It specifies the resource (npm packages) and the exact questions it answers, distinguishing it from siblings like scan_competitor_ai_presence and the deps.dev:version fallback.

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?

Explicitly states when to use: whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also provides an exclusion: NPM ecosystem only in v1, with PyPI/Maven/Cargo/Go falling under deps.dev:version directly, giving a clear alternative.

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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