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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint (all safe). Description adds valuable behavioral context: '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.' 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?

Well-structured: starts with core purpose, then usage guidance, ecosystem limitations, failure behavior. Every sentence adds value. Slightly long but not wasteful; could be slightly more concise without losing clarity.

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?

Despite lacking an output schema, the description thoroughly lists all return fields (summary block, per-advisory detail, links, alternative versions). It covers failure modes and timing. For a 2-parameter tool with 100% schema coverage and full annotations, this is complete.

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% for both parameters, so baseline is 3. Description adds meaning beyond schema: explains that 'version' defaults to latest when omitted, accepts scoped packages. Also describes the return fields in detail, compensating for lack of output schema. This extra context justifies a 4.

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 a specific verb+resource: composite check for npm package safety/size/popularity. It distinguishes from siblings (none are npm-specific) and describes the integrated services (deps.dev, bundlephobia).

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: 'is X safe / popular / small' or 'what does adding lodash cost me'. Also provides exclusions: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly.' This gives clear alternatives.

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

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.

Resources