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Glama

Wikidata

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?

Annotations already declare readOnly, idempotent, safe. Description adds critical behavioral context: mentions fan-out to two services, partial failure with graceful degradation, and a 5-30s delay for new versions on bundlephobia. 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?

Description is detailed but front-loaded with the core purpose. Some redundancy could be trimmed, but every sentence adds value. It is well-structured and informative.

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?

Although no output schema, the description fully explains the return format including summary block fields, per-advisory detail, links, and alternative versions. Error handling is also mentioned. Given the tool's complexity, the description is highly complete.

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 description coverage is 100% with clear descriptions for both params. Description does not add significant new meaning beyond what the schema already provides. Baseline 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's a composite check for evaluating npm packages, covering license, advisories, bundle size, etc. It distinguishes itself from siblings by specifying 'npm ecosystem only' and directing other ecosystems to a different tool.

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 'Use whenever an agent asks is X safe / popular / small'. Also clarifies when not to use: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly', providing clear alternatives.

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

A3.5/5.0
Disambiguation2/5

The tool set is extremely heterogeneous and overlapping. Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and entity-related tools (get_entity, get_wikidata_facts, entity_profile, compare_entities, resolve_entity) have unclear boundaries. The server is named Wikidata but only 3 of 34 tools actually relate to Wikidata, causing confusion about which tool is appropriate.

Naming Consistency2/5

Tool names mix multiple conventions: verb_noun (get_entity, search_entities, list_subscriptions), noun phrases (polymarket_edges, entity_profile, pipeworx_feedback), and inconsistent suffixes (_beta, _grounded). The verbs used are vague and not part of a coherent pattern (get, search, ask, discover, resolve, scan, validate, generate, remember, recall). The naming is readable but lacks a predictable system.

Tool Count2/5

34 tools is high for a server with a focused name like Wikidata, and nearly all tools belong to unrelated domains (Pipeworx data routing, Polymarket betting, memory management, subscriptions, AI visibility). The count appears bloated and scattered rather than well-scoped; most tools could be split into separate servers.

Completeness2/5

For a Wikidata server, the coverage is minimal: only search, get by ID, and human-readable facts are present, with no SPARQL query support or property-level access. The majority of tools address Pipeworx's broad data catalog, leaving the advertised Wikidata purpose severely under-served. The surface is complete for a different domain but incomplete for the stated one.

Resources