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Glama

Inegi

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

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds significant behavioral context: it fans out to external services (deps.dev and bundlephobia), notes that bundlephobia's first measurement can take 5-30 seconds, and explains how partial failures are handled gracefully with sources_failed field. 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 efficiently structured: starts with composite purpose, lists what it covers, gives usage examples, specifies ecosystem scope, and ends with failure behavior. Every sentence provides essential information with no redundancy or 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?

Despite complexity (composite call, multiple sources, no output schema), the description thoroughly covers return fields (summary, advisories, links, alternatives), ecosystem boundary, and failure modes. It provides enough context for an AI agent to understand what to expect and how the tool behaves.

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 clear descriptions for both parameters. The description adds extra context beyond schema: it notes that scoped packages (@types/node) are accepted, and clarifies that version defaults to latest. This adds modest value, justifying a score above 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 the tool performs a composite check for npm packages, answering questions about safety, popularity, and size. It explicitly distinguishes from other ecosystems (PyPI, Maven, etc.) by noting they fall under deps.dev:version directly, differentiating it from potential siblings.

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?

Provides explicit when-to-use guidance: 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also specifies the NPM-only scope and directs users to other tools for other ecosystems. Mentions graceful degradation for partial failures, indicating behavior in edge cases.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there are some close groups: the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) require careful reading to distinguish subtle differences.

Naming Consistency3/5

Naming is inconsistent: some tools start with verbs (ask, compare, generate), others with nouns (inegi_indicator, entity_profile), and there is no uniform verb_noun pattern. However, within families, naming is consistent (e.g., ask_pipeworx*).

Tool Count4/5

With 33 tools, the server is on the higher side but not excessive. Each tool serves a distinct purpose, though some could be merged (e.g., ask_pipeworx variants). The count is justified by the breadth of domains covered (INEGI, Pipeworx, Polymarket, utilities).

Completeness4/5

The toolset covers a wide range of functionalities: Mexican demographic/economic data, general structured data queries, prediction market analysis, and utility tools. Minor gaps exist (e.g., limited direct API for some Mexican indicators), but overall the surface is comprehensive for the stated purpose.

Resources