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Glama

Data Iledefrance

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly/ idempotent/ non-destructive; description adds critical behavioral details: composite call fan-out, bundlephobia's 5-30s timeout, partial failures with sources_failed, and graceful degradation. 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?

Single paragraph that front-loads the core purpose. Every sentence adds value, though slightly verbose. Could be tightened without losing information.

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 no output schema, the description thoroughly explains return structure (summary block, advisories, links, alternative versions) and edge cases (partial failures, ecosystem limitations). Sufficient for agent understanding.

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% with clear descriptions for both parameters. The description adds minor context (scoped packages, default to latest) but does not significantly enhance meaning beyond the schema.

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, covering license, advisories, version history, and bundle size. It uses specific language like 'safety, popularity, size' and contrasts with alternatives, distinguishing it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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') and mentions ecosystem constraints (NPM only, partial failures, timeout). Does not list sibling tools by name but provides contextual guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Iledefrance toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., multiple ask/tools for querying (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple company analysis tools (entity_profile, compare_entities, recent_changes). The prediction market tools (bet_research, polymarket_arbitrage, etc.) further blur distinctions. Agents would frequently select the wrong tool.

Naming Consistency2/5

Naming conventions are inconsistent: snake_case (ask_pipeworx, dataset_info), camelCase (ai_visibility_check, scan_competitor_ai_presence), and phrases (pipeworx_feedback, polymarket_arbitrage). No pattern emerges, making tool discovery harder.

Tool Count2/5

33 tools is excessive for a server ostensibly about Île-de-France Open Data. Only 3 tools (dataset_info, query, search_datasets) relate to that domain, while the rest are a general-purpose data agent with many disjoint capabilities. The count feels bloated and unfocused.

Completeness2/5

For the declared purpose (Île-de-France data), the tool set is missing CRUD operations (no create/update/delete). For the actual general data use, there are gaps like missing person entity resolution, data visualization, and file handling. The deep_research tool requires an account, creating a barrier.

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