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Glama

Hgnc

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

A4.6/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. Description adds behavioral context: partial failures, bundlephobia latency (5-30s), and sources_failed listing.

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?

Description is thorough but slightly long. Every sentence adds value; front-loaded with purpose. Could be more concise but not wasteful.

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?

Covers all necessary aspects: what it does, input parameters, return fields, error handling, fallback, limitations. Compensates for lack of output schema.

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. Description adds meaning: scoped packages accepted, default version behavior (latest), and implicit format expectations.

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 specifies the composite check for adding npm packages, covering safety, popularity, and size, and distinguishes from siblings by noting ecosystem scope and alternative tools.

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 (agent asks about package safety/popularity/size) and when not to use (non-npm ecosystems point 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 Hgnc toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Most tools have clearly distinct purposes, but the set is large and several boundaries are fuzzy: ask_pipeworx_beta is currently identical to ask_pipeworx, and the five polymarket_* tools plus ai_visibility_check/scan_competitor_ai_presence create selection ambiguity. The exhaustive descriptions mitigate confusion, but an agent could still easily pick the wrong variant.

Naming Consistency4/5

All tool names use snake_case and most follow a verb_noun pattern (get_gene, search_genes, validate_claim, subscribe/unsubscribe, compare_entities). The deviations are minor and internally consistent: noun-first family names like polymarket_edges/entity_profile and the ask_pipeworx_* variant group.

Tool Count2/5

34 tools is well above the comfortable range, and the server is named Hgnc while only 3 of its tools actually concern HGNC genes. The Pipeworx platform is bolted onto what should be a narrow gene-lookup surface, making the set feel bloated and mis-scoped.

Completeness4/5

For the HGNC domain, search_genes → get_gene → resolve_xref covers the core lookup lifecycle well. For the broader Pipeworx functionality the surface is remarkably thorough, with ask, grounded, deep research, compare, validate, subscribe, and memory tools leaving only minor gaps such as batch gene listing.

Resources