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Glama

Kegg

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds crucial behavioral details about partial failures and timing (bundlephobia's first measurement takes 5-30s, sources_failed list), which go beyond annotation coverage and help set agent expectations.

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 a single concise paragraph that front-loads the core purpose and then elaborates on details. It is efficient with no wasted words, though it could be broken into bullet points for readability.

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?

Given no output schema, the description thoroughly explains the return values (summary block fields, per-advisory detail, links, alternative versions) and covers edge cases (failure modes, ecosystem limits). This makes the tool fully understandable for an AI agent.

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 coverage is 100%, so the description does not need to explain parameters; it adds context about the return summary block but does not enhance parameter meaning. 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 the tool performs a composite check for evaluating whether to add an npm package, specifying data sources (deps.dev and bundlephobia) and the exact purpose. It distinguishes itself from sibling tools, which are unrelated to dependency scanning.

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 states when to use ('is X safe / popular / small', 'what does adding lodash cost me') and provides a clear exclusion ('NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), effectively guiding agent selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Kegg toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

The three ask_pipeworx variants heavily overlap, with ask_pipeworx_beta currently matching ask_pipeworx exactly, and the six polymarket_* tools have blurry boundaries between scanning, arbitrage, and fill-risk analysis. The many non-KEGG research tools also make it easy to confuse unrelated purposes with the server's nominal bioinformatics focus.

Naming Consistency3/5

All names use snake_case, but the conventions vary widely: single verbs (find, remember, subscribe), verb_noun pairs (get_entry, resolve_entity), noun phrases (entity_profile, bet_research), and vendor prefixes (pipeworx_*, polymarket_*). It is readable but not a consistent, predictable pattern.

Tool Count1/5

34 tools is far beyond the well-scoped range, and the server is named 'Kegg' while only 3 of the 34 tools actually relate to KEGG bioinformatics. The rest belong to an unrelated Pipeworx data-research and prediction-market platform, an extreme mismatch between name, purpose, and tool count.

Completeness2/5

For a KEGG server, the surface is severely incomplete: only find, get_entry, and list_database exist, with no batch retrieval, cross-database queries, or pathway-organism mapping. The broader Pipeworx toolset is rich for data research but entirely disconnected from the server's stated purpose, leaving the actual domain under-covered.

Resources