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Glama

Cataas

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?

Adds significant behavioral context beyond annotations: composite nature (fans out across two services), graceful degradation on partial failures, bundlephobia's first measurement delay (5-30s), and the sources_failed field. Annotations already indicate read-only, but the description enriches with real-world behavior.

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 informative but somewhat lengthy. It is well-structured with front-loaded purpose, usage, then behavioral details. Every sentence adds value, though minor redundancy could be trimmed.

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 fully explains the return structure: summary block fields, per-advisory detail, links, alternative versions. Also covers edge cases like partial failures and ecosystem limitations. Comprehensive for a complex 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 already describes both parameters with 100% coverage. The description adds useful context: accepts scoped packages (e.g., '@types/node') and that version defaults to latest. This goes beyond the schema but is not exhaustive.

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 it is a composite check for npm package evaluation, covering license, advisories, version history, bundle size, and tree-shaking. It differentiates from siblings by specifying the ecosystem scope and mentioning alternatives for other package managers.

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 says 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'.' Also provides guidance for when not to use it (other ecosystems) by directing to 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 Cataas toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying data catalog, and the polymarket_* family has several near-synonymous scanning tools. Entity_profile, compare_entities, and recent_changes also have fuzzy boundaries. Only the three cat tools are clearly distinct, but an agent could easily pick the wrong tool across the Pipeworx family.

Naming Consistency3/5

The names are consistently snake_case and several families share clear prefixes like ask_pipeworx, polymarket_, and list_. However, the set mixes imperative verb-first names with noun-style names, and the cat_* tools do not share any naming pattern with the dominant Pipeworx family.

Tool Count2/5

34 tools is too many for a server named Cataas, and only 3 of them are actually cat-related. The other 31 tools belong to an unrelated data-research and prediction-market platform, making the tool count inappropriate for the apparent purpose.

Completeness2/5

As a cat-image server, the surface is thin: random_cat, cat_by_tag, and list_tags cover basics but omit common Cataas operations like GIFs or text-on-cat. As a data-research platform, the cat tools are noise, so the mixed set is incomplete and incoherent for either apparent purpose.

Resources