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Glama

Datacite

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Discloses multiple behaviors: composite call, partial degradation with sources_failed list, and the 5-30 second delay for bundlephobia on new versions. The annotations already show readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, and the description adds complementary context without contradiction.

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 moderately sized but packed with essential information. It is front-loaded with the core purpose and covers alternatives, partial failures, and environment details. Minor redundancy exists but does not detract significantly.

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?

Given no output schema, the description thoroughly explains the return structure (summary block, per-advisory detail, links, alternative versions). It also covers edge cases like partial failures and timeouts, making it nearly self-contained for correct invocation.

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?

Both parameters are fully covered by the input schema (100% coverage). The description adds useful context: scoped packages accepted for 'package' and defaulting to latest for 'version'. This exceeds the baseline of 3 by providing extra semantic guidance.

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 specifies exactly what the tool does: a composite check for npm packages covering license, advisories, version history, bundle size, dependency count, and ESM/tree-shake support. It clearly distinguishes from siblings by limiting to npm and mentioning alternative tools for other ecosystems.

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 when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies when not to use: for PyPI/Maven/Cargo/Go it should fall under deps.dev:version directly.

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

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping roles: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points (beta currently behaves exactly like stable), while entity_profile, recent_changes, and compare_entities all target company research. The prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) is large enough that an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are mixed throughout: verb_noun patterns (search_dois, list_repositories, validate_claim) coexist with noun phrases (entity_profile, recent_alerts, ai_visibility_check) and bare verbs (remember, recall, forget, subscribe). The only consistent thread is snake_case, but the grammatical style is unpredictable.

Tool Count2/5

At 34 tools, the server is overstuffed, especially given its apparent focus is DataCite but only 3 tools actually serve that domain (get_doi, search_dois, list_repositories). The rest are a grab bag of Pipeworx data routing, prediction-market analytics, memory, and subscription utilities that would be better split into separate focused servers.

Completeness2/5

The DataCite surface is incomplete (no DOI creation, update, or deletion despite DataCite supporting registration), while the Pipeworx surface has no direct fetch-by-URI tool for the pipeworx:// citations that other tools return. The presence of a duplicate beta router and a beta router with no active differences further muddies the coverage picture.

Resources