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Glama

Data Nantes

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by detailing partial failure modes (bundlephobia first measurement can take 5-30s, sources_failed listed), which supplements the annotations. No contradictions.

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 relatively long but front-loaded with the main purpose. Each sentence adds value (behavior, limitations, return content). Could be slightly more concise, but no fluff.

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?

Without an output schema, the description thoroughly lists return fields (summary block, per-advisory detail, links, alternative versions). It also explains partial failure behavior and ecosystem limitations, making it complete for the agent.

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 has 100% description coverage for both parameters. Description adds practical context: scoped packages accepted (e.g., @types/node), version defaults to latest. This goes beyond schema, providing useful usage guidance.

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 packages, combining deps.dev and bundlephobia. It uses specific verbs ('scan', 'check') and identifies the resource (npm package). Sibling tools include various unrelated tools; this tool's purpose is distinct.

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 advises when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes scope limitations (NPM ecosystem only in v1) and handles partial failures gracefully, providing guidance on expected behavior.

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

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions are detailed, the similarity in function could confuse an agent trying to select the right one.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, ask_pipeworx, bet_research, compare_entities). No mixing of conventions (camelCase, PascalCase) is observed, making it predictable.

Tool Count3/5

With 34 tools, the server is on the heavy side. The majority are Pipeworx tools covering many domains, but the count exceeds the typical sweet spot of 3-15 tools. Some consolidation could reduce redundancy.

Completeness4/5

The tool surface covers a wide range of data access and analysis (Nantes open data, SEC filings, Polymarket, entity comparison, dependency scanning). Minor gaps exist, such as limited Nantes data operations (only query and info) and some tools requiring external accounts, but the overall scope is comprehensive.

Resources