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Glama

Pexels

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds valuable behavioral details beyond annotations: partial failures degrade gracefully, first bundlephobia measurement can take 5-30s, and sources_failed will list timeouts. 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.

Conciseness5/5

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

The description is long but exceptionally dense; every sentence earns its place: purpose, trigger phrasing, output summary fields, ecosystem scope, and failure behavior are all covered. It is well-structured and front-loaded with the core purpose.

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?

For a composite tool with no output schema, the description compensates by enumerating return fields, listing failure modes, and noting latency. Combined with strong annotations and full schema coverage, the agent has enough context to invoke and interpret the tool correctly.

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 covers both parameters fully (package name and version with defaults and scoped-package acceptance). The description adds example usage ('lodash') but no new semantic meaning beyond the schema, so 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 a composite purpose: to check if an npm package is worth adding by aggregating deps.dev and bundlephobia data. It distinguishes from siblings by naming data sources and specifying NPM-only scope, making the tool's function unambiguous.

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 is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also excludes non-NPM ecosystems by directing them to deps.dev:version directly, offering clear when-to-use and when-not-to-use context.

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

A3.9/5.0
Disambiguation3/5

Many tools are clearly from distinct domains (Pexels media, Pipeworx data, Polymarket betting, utility), but within each domain there is significant overlap (e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research, or numerous polymarket tools). Detailed descriptions help differentiate, but some tools could still be confused.

Naming Consistency3/5

Naming conventions vary: some tools use verb_noun (ask_pipeworx, compare_entities), others are single-word (photo, video), and some mix patterns (photo_curated vs photo_search). The Pexels subset is consistent, but the overall set includes tools from other ecosystems with different styles.

Tool Count2/5

With 38 tools, the set is bloated for a Pexels server. Only 8 tools are actually Pexels-related; the rest are Pipeworx data query, Polymarket betting, and utility tools. This mismatch makes the count inappropriate for the stated server purpose.

Completeness4/5

For the Pexels domain, the tool set covers search, fetch, and collections for both photos and videos, which is adequate. The unrelated tools add bloat but don't create gaps in the Pexels functionality. Minor missing features like uploads or editorial content are not critical.

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