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Glama

Gtin

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint false. Description adds valuable behavioral context: composite call fans out, partial failures degrade gracefully, bundlephobia first measurement may take 5-30s and will be listed in sources_failed if timed out.

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 every sentence adds specific information: purpose, usage, return structure, ecosystem limitation, failure behavior. Could be slightly more concise but 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?

Despite no output schema, description lists all return fields (summary block fields: is_latest, license, published_at, etc.), per-advisory detail, links, alternative versions. Covers inputs, behavior, and outputs completely for a two-parameter tool.

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 covers both parameters with descriptions (100% coverage). Description adds that scoped packages are accepted and version defaults to latest, providing minor extra value 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?

Description clearly states the composite purpose: 'should I add this npm package to my project' check aggregating deps.dev and bundlephobia data. It distinguishes itself from siblings by being the only dependency scanning 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?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes ecosystem limitation and alternatives: '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.

All Gtin toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve a query-routing/research function, with ask_pipeworx and ask_pipeworx_beta being currently identical. The five polymarket_* tools also share boundaries, making it genuinely ambiguous which one to pick for a given betting question.

Naming Consistency2/5

The set mixes verb-style names (forget, recall, subscribe, unsubscribe), noun-style names (entity_profile, recent_changes, bet_research), and brand-prefixed families (ask_pipeworx*, polymarket_*). There is no single verb_noun pattern, and conventions differ across families even though individual families are internally consistent.

Tool Count2/5

With 33 tools, the count is high, and the server is named 'Gtin' yet only two tools relate to barcodes/GTIN — the rest form a sprawling data-research and prediction-market toolkit. Many tools could be consolidated (e.g., the ask_pipeworx variants, the polymarket suite), so the surface feels heavier than its core purpose requires.

Completeness4/5

For the actual domain revealed by the tools — multi-source structured data lookup, entity profiling, prediction-market analysis, and agent memory — the surface is quite complete: it covers query routing, grounded verification, comparisons, research, subscriptions, memory, and feedback loops. However, given the server name 'Gtin', the barcode domain is severely under-covered (only validation and check digit, no lookup or product data), which prevents a perfect score.

Resources