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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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, etc. Description adds significant behavioral context: fans out to external services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, sources_failed lists timeouts. No contradictions.

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 packed with useful information and front-loaded with purpose and usage. Some sentences could be tighter, but overall efficient for the complexity level.

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 the tool's complexity (composite, external services, partial failures, multiple output fields) and absence of output schema, the description thoroughly covers behavior, output structure, timing caveats, and ecosystem limitations.

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%; description adds only minor additional context (scoped packages accepted, version defaults to latest), largely repeating schema descriptions. Meets baseline for high coverage but adds little extra value.

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 is a composite check for evaluating whether to add an npm package, fanning out across deps.dev and bundlephobia. It distinguishes from sibling tools by focusing on npm dependencies and listing specific output fields.

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 provides when to use (e.g., agent asks about safety/popularity/size) and when not to use (non-npm ecosystems, with alternative suggestion). Covers both inclusion and exclusion criteria.

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

A3.9/5.0
Disambiguation2/5

Multiple tools appear to do nearly the same thing, notably ask_pipeworx and ask_pipeworx_beta (the description explicitly says they currently match exactly), plus ask_pipeworx_grounded and deep_research which are all variations of the same routing/query capability. The Polymarket tools and the AI-visibility tools also have overlapping boundaries, making it easy for an agent to pick the wrong one.

Naming Consistency4/5

Most tools follow a readable snake_case verb_noun pattern like ask_pipeworx, compare_entities, resolve_entity, scan_dependency, and validate_claim. There are minor deviations such as entity_profile, bet_research, pipeworx_feedback, and recent_changes, but the naming is largely predictable and clearly grouped by prefixes like polymarket_ and pipeworx_.

Tool Count2/5

With 33 tools, this server exceeds the threshold where the count becomes a burden rather than a benefit. The set covers many disparate domains—general data querying, Polymarket betting, plant taxonomy, npm auditing, AI visibility, memory, and subscriptions—so the surface feels over-scoped for a single server.

Completeness4/5

The core research/query workflow is well covered: discovery, lookup, grounded answers, deep research, entity resolution, comparison, validation, and change tracking are all present. Subscription and memory lifecycles are also complete; the main gaps are minor, such as no explicit tool for fetching a pipeworx:// citation URI directly.

Resources