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Glama

Pypi

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds behavioral context: fans out across two services, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed list. 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?

Description is somewhat verbose but well-structured; front-loaded with purpose. Could be more concise but effectively communicates key information in 3-4 sentences. 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?

Given complexity (multiple data sources, fallbacks, timing), description covers return fields (summary block, advisories, links, alternative versions) and failure behavior. No output schema, but description sufficiently informs agent of expected output structure.

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% with clear parameter descriptions. Description does not add significant extra parameter meaning beyond what schema provides. Baseline 3 applies.

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 composite check for npm package covering safety, size, and popularity. Distinct from siblings like get_package and get_package_version, which are simpler lookups. Specifies ecosystem scope (npm only in v1).

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: when agent asks 'is X safe/popular/small' or 'what does adding X cost'. Also notes limitations: NPM only; other ecosystems fall under different tool. Provides alternative guidance for other ecosystems.

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

All Pypi toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently exactly the same behavior, and polymarket_edges, polymarket_arbitrage, and bet_research all present as 'find a betting opportunity' scanners. The detailed descriptions help, but an agent still needs careful triage to avoid picking the wrong entry point.

Naming Consistency3/5

The set is uniformly snake_case and many tools follow verb_noun (get_package, list_releases, resolve_entity), but conventions are split between product-prefixed families (ask_pipeworx, pipeworx_feedback, polymarket_*), noun-led names (entity_profile, recent_alerts, deep_research), and bare-verb memory tools (remember, recall, forget). The result is readable but not a single predictable pattern.

Tool Count2/5

35 tools is well into the overgrown range, and the count is especially mismatched for a server labeled Pypi: only a handful of tools actually relate to Python packages, while the rest cover Pipeworx data lookup, prediction markets, AI visibility, memory, and subscriptions. This looks like several unrelated tool surfaces merged under one server.

Completeness3/5

For the broad Pipeworx data-research surface, coverage is strong: lookup, grounded answers, deep research, entity profiles, comparisons, validation, subscriptions, and memory are all represented. However, for a PyPI-focused server the surface is incomplete—there is no package search, upload, or account/maintainer tooling—and the PyPI tools feel like an afterthought.

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