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Edamam

by io.github.pipeworx-io

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare idempotent and read-only. Description adds significant behavioral context: fans out across two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s. 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.

Conciseness4/5

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

Description is somewhat long but every sentence adds value. Front-loaded with the key composite nature, then details. Could be slightly more concise but appropriately structured for the information density.

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 no output schema, the description fully explains what is returned (summary block, per-advisory detail, links, alternative versions) and covers edge cases (timeouts, partial failures). Comprehensive for the tool's complexity.

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 coverage is 100% with descriptions for both parameters. Description adds extra value: scoped packages accepted, version defaults to latest. This exceeds the baseline of 3.

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 it's a composite check for npm packages covering license, advisories, version history, bundle size, etc. The verb 'scan' and resource 'dependency' are specific, and the description distinguishes it from sibling tools like 'scan_competitor_ai_presence'.

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 says 'Use whenever an agent asks...' and provides clear conditions for when to use and when not to use (NPM ecosystem only in v1; alternatives for other ecosystems).

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

A3.6/5.0
Disambiguation2/5

The tool set includes multiple overlapping tools (ask_pipeworx variants, many polymarket tools) that serve similar purposes, and there is a sharp domain split between food tools and Pipeworx data tools, making it hard for an agent to choose correctly.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun (search_food), noun_verb (nutrition_analysis), prefixed (pipeworx_feedback, polymarket_arbitrage), and no pattern. Some use underscores, some use whole words.

Tool Count3/5

34 tools is on the high side, and the scope is extremely broad (food, data queries, prediction markets, subscriptions), which could overwhelm an agent, but the number alone is not extreme.

Completeness2/5

As a food API server, it is missing key features like recipe details, ingredient substitution, or meal planning, while including many extraneous tools. The data analytics tools are extensive but not aligned with the server's stated purpose.

Resources