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Glama

Data Toulouse

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?

Beyond the annotations (readOnly, idempotent), the description adds valuable behavioral details: composite fan-out, partial failure handling, bundlephobia delay of 5-30s, and sources_failed reporting. This provides a clear picture of expected behavior.

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?

The description is concise at 4 sentences, but it is dense with information. It front-loads the composite nature and use case, then details return fields and edge cases. Minor room for structuring but overall effective.

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 lists return fields, mentions per-advisory detail and alternative versions, and covers failure modes like timeouts. For a composite tool with multiple data sources, this is complete guidance.

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?

The schema already has 100% coverage with clear descriptions for both parameters. The description does not add significant new meaning, only restates that scoped packages are accepted, which is already in the schema. Baseline of 3 is appropriate.

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 deciding whether to add an npm package, aggregating data from deps.dev and bundlephobia. It distinguishes itself from sibling tools like resolve_entity and scan_competitor_ai_presence by specifying the exact use case.

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 says to use when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also notes NPM ecosystem only and directs other ecosystems to use deps.dev:version directly, preventing misuse.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread). Users will struggle to choose the right tool without deep reading.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, bet_research), others camelCase (generate_llms_txt, list_subscriptions), and many are long phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern.

Tool Count3/5

34 tools is high but not extreme. The server covers diverse domains (company data, drugs, economics, prediction markets, open data, memory utilities), but many tools are very specific and could be consolidated, making the set feel bloated.

Completeness3/5

The server covers many data sources but has notable gaps: simple market listing tools are missing for Polymarket, and the Toulouse Open Data tools are limited to querying only (no create/update/delete). Memory and subscription tools seem ancillary to the core data mission.

Resources