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Quotes

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

A4.9/5.0
Behavior5/5

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

The description adds behavioral context beyond annotations (readOnlyHint, idempotentHint) by detailing partial failure handling, timeout behavior (5-30s for new versions), and the fact that sources_failed lists timed-out sources while returning other data.

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 a single paragraph but efficiently conveys purpose, usage, and behavior. It is slightly dense but well-organized with clear clauses. Loses a point for being a bit long.

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 (composite tool) and absence of output schema, the description thoroughly covers return values (summary block, details, links, alternatives), ecosystem scope, error handling, and timeout behavior, leaving no ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds extra value: scoped packages like '@types/node' are accepted, and version defaults to latest when omitted. Also describes the output structure.

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 the composite check for npm packages, specifying the exact question it answers ('should I add this npm package'), and distinguishes it from sibling tools by naming the data sources (deps.dev, bundlephobia) and the ecosystem (NPM).

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 tells when to use: 'whenever an agent asks "is X safe / popular / small"', and when not to use: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', providing clear alternatives.

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

A3.8/5.0
Disambiguation3/5

The tool set includes multiple similar tools (e.g., three ask_pipeworx variants, several Polymarket tools) that could cause agent confusion. While each has a distinct purpose, the boundaries are subtle and descriptions lean heavily on jargon, making misselection likely.

Naming Consistency4/5

All tool names use snake_case consistently. Most follow a noun_verb or verb_noun pattern, but some (e.g., ai_visibility_check, bet_research) start with a subject rather than an action, breaking a strict verb-first convention.

Tool Count2/5

With 40 tools, the server feels overloaded. The name 'Quotes' suggests a narrow focus, yet the tool set spans fact-checking, company research, prediction markets, and more. Many tools are highly specialized or meta-tools, inflating the count without clear necessity.

Completeness3/5

The server covers a wide range of use cases, from quotes and literature to financial data and prediction markets. However, there are noticeable gaps in core areas (e.g., basic CRUD for quotes beyond search and random), and the sheer breadth creates dead ends for deep workflows.

Resources