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Overpass

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

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, non-destructive. The description adds valuable behavioral context beyond that: partial failure degradation, bundlephobia's first measurement may take 5-30s, and sources_failed listing timeouts. 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.

Conciseness5/5

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

The description is a single coherent paragraph where every sentence contributes essential information: purpose, sources, use cases, output summary fields, ecosystem scope, and failure behavior. It is front-loaded with the core purpose and remains readable despite its length.

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?

This is a complex composite tool with multiple external services and edge cases. The description covers the output summary block, per-advisory details, links, alternative versions, ecosystem limitations, and performance expectations. Without an output schema, this level of detail is essential and sufficient.

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 baseline is 3. The description adds no new parameter semantics beyond what the schema already provides (package name, version defaults to latest). It does not compensate with extra detail, hence no increase above baseline.

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 and specifically defines the tool as a composite 'should I add this npm package' check that fans out across deps.dev and bundlephobia. It distinguishes from alternatives by noting NPM-only scope and directing other ecosystems to deps.dev:version directly, which separates it from sibling tools.

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...' and it states when not to use it (non-NPM ecosystems go to deps.dev:version directly). This clearly communicates when to invoke this tool versus 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

Most tools have distinct purposes, but the ask_pipeworx trio (stable, beta, grounded) are near-identical variants, and the five polymarket_* tools plus bet_research heavily overlap in the edge-finding space. Detailed descriptions help, but an agent could easily misselect among these clusters.

Naming Consistency3/5

Names are all snake_case and readable, but conventions vary: ask_pipeworx_* uses a prefix pattern, polymarket_* is consistent, yet others mix verbs (scan_competitor_ai_presence, generate_llms_txt) with nouns (entity_profile, resolve_entity). No single verb_noun pattern governs the set.

Tool Count2/5

34 tools is well above the 25-tool threshold and feels like multiple servers merged into one: structured data routing, prediction markets, OSM, memory, subscriptions, and AI-visibility checks. The breadth is impressive but the count is heavy for a single tool surface.

Completeness4/5

The surface is notably complete for its blended domain: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe, data access has multiple router modes plus deep research and claim validation, and prediction markets have research, edge, arbitrage, fill-risk, and cross-venue tools. Minor gaps exist (no subscription update, no explicit reverse-geocoding tool), but core workflows are covered.

Resources