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Glama

Data Paris

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

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, openWorld), description adds that bundlephobia first measurement can take 5-30s and may timeout, with sources_failed listing it. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with front-loaded purpose: first sentence composite check, second usage, third return format, fourth ecosystem, fifth caveats. Slightly long but every sentence adds value.

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?

No output schema, but description fully enumerates return fields plus per-advisory, links, alternatives. Covers edge cases like partial failures and timeouts. Complete for a composite 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 covers both parameters fully. Description adds useful detail: scoped packages accepted and version defaults to latest, which goes above baseline of 3 for high coverage.

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?

Description clearly states it is a composite check for adding npm packages, fans out across deps.dev and bundlephobia. Distinguishes from siblings by specifying NPM ecosystem and composite nature.

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 to use when agent asks about safety, popularity, size, or cost. Notes NPM-only in v1 and directs other ecosystems to deps.dev:version directly. Also explains partial failure degradation.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct function or data domain, from prediction markets (bet_research, polymarket_*) to company research (entity_profile, compare_entities) to data queries (query, dataset_info). No two tools have overlapping purposes; meta-tools like ask_pipeworx and deep_research are clearly differentiated.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive verbs (e.g., search_datasets, validate_claim, remember). No mix of conventions like camelCase or inconsistent verb choices.

Tool Count4/5

At 33 tools, the server covers a broad range of capabilities (data retrieval, AI visibility, prediction markets, company profiles, subscriptions, etc.). While slightly above the ideal range, the count is justified by the breadth of functionality and no tool feels redundant.

Completeness4/5

The tool set provides comprehensive coverage for data discovery, retrieval, and analysis across multiple domains (SEC, FDA, FRED, Paris Open Data, prediction markets, etc.). Minor gaps exist (e.g., no update/delete for Paris Open Data), but the primary focus on reading and analysis is well-served.

Resources