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Glama

Discogs

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?

Annotations already declare readOnly, idempotent, not destructive. Description adds critical context on partial failure (graceful degradation, 5-30s timeout for bundlephobia first measurement, sources_failed field) and return structure.

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 front-loads purpose. Every sentence adds value, though slightly long. Could be broken into two sentences for readability.

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 comprehensively details return fields (summary, advisories, links, alternative versions) and handles edge cases (partial failures, timing). Complete for agent decision-making.

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 already covers both parameters fully (100% coverage). Description adds useful context: scoped package acceptance and default version behavior, enhancing schema meaning.

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 composite one-call check for npm package evaluation, specifying the exact resources (deps.dev, bundlephobia) and outputs. It distinguishes itself from siblings by being the only npm dependency scanner.

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 ('is X safe / popular / small') and when not ('NPM ecosystem only in v1; PyPI/Maven/Cargo/Go 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

A3.6/5.0
Disambiguation1/5

There is severe overlap among tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and deep_research, discover_tools, and suggest_questions all serve discovery/research purposes. The Discogs-specific tools are distinct, but the large number of redundant Pipeworx tools makes it impossible to tell which one to pick.

Naming Consistency2/5

The Discogs tools follow a clean verb_noun pattern (get_artist, get_label, get_master, get_release), but the majority of the set uses inconsistent, domain-specific names (deep_research, generate_llms_txt, polymarket_arbitrage, scan_competitor_ai_presence). No single naming convention is applied across the server.

Tool Count1/5

With 36 tools, the server is massively over-provisioned for a Discogs-focused API. Most tools (e.g., ask_pipeworx, polymarket_edges, SEC lookups) have nothing to do with Discogs and belong to a separate service. The Discogs surface alone could be served by ~6 tools (search, get_artist, get_label, get_master, get_release, search_within), so the count is wildly inappropriate.

Completeness3/5

For the Discogs domain, the core entities (artist, label, master, release) and full-text search are present, plus semantic search inside records. However, there are gaps like user collections, wantlists, marketplace, and discogs-specific filters beyond format/country. The presence of many unrelated tools does not directly hurt domain coverage, but the Discogs surface is not exhaustive.

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