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Glama

Eurostat

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

A4.7/5.0
Behavior5/5

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

Description adds significant context beyond annotations: fans out across multiple APIs, partial failures degrade gracefully, mentions bundlephobia's first measurement can take 5-30s, and explains sources_failed behavior. No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense single paragraph with all necessary information. Could be more structured (e.g., bullet points for return fields), but no redundant sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity and no output schema, description adequately covers return summary fields, per-advisory details, links, alternative versions, and ecosystem limitations. Lacks explicit mention of error handling beyond timing issues.

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. Description adds value by noting version defaults to latest and package accepts scoped packages. Minor additional context beyond 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 starts with a specific verb 'scan' and resource 'dependency', clearly stating it's a composite check for npm packages using deps.dev and bundlephobia. It distinguishes from siblings by noting ecosystem limitations and alternatives.

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 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and clarifies 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', providing clear when-to-use and when-not-to-use.

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

All Eurostat toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

The set is heavily overlapped: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all serve similar factual-lookup purposes with fuzzy boundaries. The Polymarket tools and visibility tools also overlap substantially, making tool selection genuinely ambiguous despite long descriptions.

Naming Consistency3/5

All names are lowercase snake_case, which is internally consistent, but there is no predictable verb_noun pattern: verb styles vary wildly (ask, get, list, search, recall, remember, forget, subscribe). The 'ask_pipeworx_beta' suffix also breaks naming convention.

Tool Count1/5

34 tools is far too many for a server named Eurostat, and only 3 of them (get_dataset, list_datasets, search_datasets) actually serve Eurostat data. The rest is a sprawling generic Pipeworx utility surface including prediction markets, memory, subscriptions, visibility checks, and dependency scanning, which is a severe scope mismatch.

Completeness2/5

For the Eurostat-specific portion, search/list/get covers basic dataset retrieval, but the server's broader surface is a grab-bag of unrelated capabilities with no coherent domain. The true domain is unclear, and the Eurostat side lacks deeper operations like metadata lookup or bulk/time-series expansion.

Resources