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Glama

dogceo

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 annotations (read-only, open-world, idempotent), the description discloses composite behavior, partial failure degradation ('sources_failed will list it if it times out'), and potential latency ('bundlephobia's first measurement... can take 5-30s'). 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 every sentence adds value. It is front-loaded with the core purpose, then usage, return fields, ecosystem scope, and error behavior. No redundancy or filler.

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?

Even without an output schema, the description enumerates all return fields (is_latest, license, published_at, etc.), lists alternative versions, and explains fallback behavior for non-npm ecosystems. It is complete for a complex tool with multiple data sources.

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 description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema for the 'package' and 'version' parameters, though it implicitly relates version to defaulting to latest in the schema. No further clarification is provided in the description.

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 tool's purpose: a composite check for npm packages covering license, advisories, bundle size, and tree-shaking. It uses a specific verb ('scan') and resource ('npm package'), and distinguishes itself from siblings by combining deps.dev and bundlephobia data in one call.

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 gives an exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', clearly indicating when not to use this tool.

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

Several tools deliberately overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the dog-photo trio plus a cluster of six prediction-market tools creates real selection ambiguity. Although descriptions are detailed, an agent could easily call the wrong variant.

Naming Consistency3/5

All names are consistently snake_case and readable, with useful domain prefixes like polymarket_ and ask_pipeworx_. However, conventions are mixed: compare_entities is verb-first, entity_profile is noun-first, bet_research is object-verb, and random_image is adjective-noun.

Tool Count2/5

35 tools is too many for a server whose name suggests a simple dog-photo service, and most tools are unrelated to that identity. The scatter across dog images, deep data research, prediction markets, npm scanning, memory, and llms.txt generation makes the set feel bloated rather than comprehensive.

Completeness3/5

The dog-image functionality is complete, and the subscription and memory lifecycles have paired operations. However, the server's true domain is incoherent, so completeness is difficult to assess; there are no major dead-ends within each cluster, but the unrelated utility tools create large topical gaps relative to the apparent dogceo identity.

Resources