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Glama

Rhea

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 declare readOnly, idempotent, not destructive. Description adds detailed behavioral context: fans out to deps.dev and bundlephobia, returns summary and details, warns about bundlephobia timing (5-30s), and graceful degradation with sources_failed list.

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 well-structured: composite action first, then usage, returns, ecosystem scope, failure behavior. Every sentence adds value; no fluff.

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 thoroughly enumerates return fields (summary block, per-advisory detail, links, alternatives), covers ecosystem scope, and explains failure modes. Complete for agent to understand and use correctly.

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 beyond schema: acceptance of scoped packages and default to latest version when omitted, which aids correct usage.

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 assessing an npm package's suitability, with specific verb 'check'. It distinguishes itself from sibling tools by focusing on npm ecosystem and a multi-source composite query.

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: when agent asks about safety, popularity, or size cost. Also provides exclusion: NPM ecosystem only; other ecosystems fall under deps.dev:version directly. Partial failure handling is also described.

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
Disambiguation4/5

Most tools have clearly distinct purposes, with only minor overlap between similar variants (e.g., ask_pipeworx variants) and related prediction market tools (bet_research vs polymarket_edges). Overall, an agent can reliably select the right tool.

Naming Consistency3/5

Tool names mix verb_noun (e.g., resolve_entity), noun_noun (e.g., entity_profile), and adjective_noun (e.g., recent_changes) patterns, plus a few single verbs. While readable, the lack of a uniform convention adds ambiguity.

Tool Count3/5

33 tools is large but justified by the broad domain coverage. The count is borderline high but still manageable with meta-tools (ask_pipeworx, deep_research) that reduce the effective surface.

Completeness4/5

The tool set covers a wide range of data sources and tasks, with meta-tools providing deep coverage. Minor gaps exist (e.g., no dedicated Kalshi-only tools, limited attention to non-Polymarket prediction markets), but the overall surface is thorough.

Resources