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Public Suffix List

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

A4.8/5.0
Behavior5/5

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

Adds important behavioral details beyond annotations: fans out across two services, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed lists failures. 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?

A single, dense sentence that packs a lot of information (use case, services, return fields, limitations) without fluff. Could be slightly more structured, but very efficient.

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 no output schema, the description thoroughly explains the return: summary block with specific fields, per-advisory detail, links, recent alternatives, and partial failure behavior. Fully adequate for the tool's complexity.

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 coverage is 100%, and description adds extra context: default for version is latest, scoped packages are accepted. This provides moderate added value beyond the schema.

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's a composite check for npm packages, combining deps.dev and bundlephobia to answer 'should I add this npm package?' It distinguishes from siblings by specifying the npm ecosystem focus and listing other tools for other ecosystems.

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 an agent asks about safety, popularity, size, or cost of adding an npm package. Also notes that other ecosystems are handled by different tools, providing clear exclusions.

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

C2.9/5.0
Disambiguation2/5

Multiple tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all query data in similar ways. Prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) are numerous and confusingly similar. Agents will struggle to choose the right tool.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use snake_case like ai_visibility_check, others are generic single words (parse, remember, forget). There is no uniform verb_noun structure, mixing descriptive names (entity_profile) with vague ones (list_version).

Tool Count3/5

35 tools is a large set for a server named 'Public Suffix List', but the actual domain (comprehensive data platform) may justify many tools. However, the count feels heavy for the apparent scope of the server, with many niche prediction market tools.

Completeness4/5

The tool surface covers a wide range: data query, comparison, subscription, memory management, and claim verification. However, there are gaps in data modification (no update/delete for records) and some prediction market features have no direct counterparts. Overall, the set is fairly complete for its data-fetching purpose.

Resources