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Pypi Stats

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context beyond this: partial failures (bundlephobia timeout), sources_failed list, and the composite nature (fans out across services). No contradictions 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?

The description is fairly verbose but well-structured: it front-loads the core purpose in the first sentence, then elaborates on details. Every sentence adds value, though some minor trimming could improve conciseness without losing information.

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?

For a composite tool with no output schema, the description fully explains the return content (summary block fields, per-advisory details, links, alternative versions). It also covers failure modes, ecosystem scope, and the multi-service fan-out, making it complete for an agent to invoke correctly.

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 both parameters are described in the schema. The description adds minor context (scoped packages accepted, default version is latest), but the schema already provides sufficient semantics for an agent to understand the parameters.

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 deciding whether to add an npm package, covering license, advisories, version history, bundle size, dependencies, ESM/tree-shake. It distinguishes from sibling tools by specifying the NPM ecosystem scope and directing other ecosystems to a different tool.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes what is not covered (PyPI, Maven, etc.) and offers an alternative for those ecosystems, helping the agent decide when not to use this tool.

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

A3.9/5.0
Disambiguation2/5

There is significant overlap between tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all performing similar data lookup functions. Additionally, multiple prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have overlapping purposes. An agent would struggle to choose the correct tool without deep understanding of subtle differences.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern and use clear domain prefixes (ask_pipeworx, polymarket_, python_) and verb_noun structure (e.g., validate_claim, compare_entities). Minor inconsistency exists with tools like 'overall' not following verb_noun, but overall pattern is predictable.

Tool Count3/5

With 36 tools, the count is high but justifiable given the broad array of capabilities (data queries, prediction markets, memory, subscriptions). However, the server name 'Pypi Stats' suggests a narrow focus, making the count feel excessive for that purpose. The actual scope is wide, so the count is borderline appropriate.

Completeness4/5

For its actual scope as a data query and analysis platform, the tool set is quite complete: it covers company profiles, comparisons, claim verification, trend analysis, and prediction market insights. Minor gaps exist (e.g., no update/delete for most data types), but core query and lookup operations are well covered.

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