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Glama

Unsplash

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?

Beyond the readOnly/idempotent annotations, it reveals composite fan-out, graceful degradation, 5-30s bundlephobia first measurement, and sources_failed behavior. It also notes NPM-only v1 limitation, adding operational context.

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?

Despite length, the description is dense and well-structured: core purpose, usage triggers, return fields, ecosystem limit, failure behavior. No filler; each clause adds information.

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, it enumerates return fields, links, alternatives, and error behavior. Combined with annotations, an agent can invoke accurately and set expectations.

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 package and version with full descriptions including scoped packages and default behavior. The description adds no extra parameter-level semantics beyond 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 opens with a specific composite check for npm packages, naming both deps.dev and bundlephobia and the exact data points. This clearly differentiates it from sibling tools like scan_competitor_ai_presence.

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?

It provides direct usage triggers ('is X safe / popular / small' or 'what does adding lodash cost me') and explicitly excludes non-npm ecosystems, pointing to deps.dev:version as the alternative. That's effective when-to-use and when-not-to-use guidance.

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

The set mixes two unrelated domains (Unsplash photos and Pipeworx data services), creating confusion about the server's purpose. Within each domain tools are mostly distinct, but several near-duplicates exist (ask_pipeworx variants, multiple polymarket scanners) and the Unsplash cluster has overlapping list/get patterns.

Naming Consistency2/5

No consistent naming convention: Unsplash tools use bare nouns, plurals, verb_noun, and noun_photo compounds; Pipeworx tools mix verb phrases (resolve_entity), noun phrases (entity_profile), and vendor-prefixed names (polymarket_edges).

Tool Count1/5

46 tools is far beyond the scope of an Unsplash server; over two-thirds belong to a different service. The tool count is unwieldy and indicates a bundled, unfocused collection.

Completeness4/5

The Unsplash-specific surface covers the public API well: search, listing, fetching by ID, random, collections, topics, user data, like/photo lists, statistics, and download tracking. Missing write operations (upload, update) are unavailable in the public API, so no dead ends for allowed workflows.

Resources