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Codewars

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 readOnlyHint=true, idempotentHint=true, etc. Description adds significant context: partial failure behavior (sources_failed list), timing caveat (5-30s first measurement), and graceful degradation. 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.

Conciseness5/5

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

Description is well-structured: front-loaded with composite nature and purpose, followed by usage, return fields, ecosystem limitation, and failure behavior. Every sentence adds value with zero waste.

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, description explains return fields (summary, advisories, links, alternatives) and failure modes. It fully covers the tool's behavior for a composite multi-source call.

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% with descriptions for both parameters. Description does not add meaning beyond schema (e.g., default version behavior is already in schema). Baseline score of 3 is appropriate.

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 it is a composite check for npm packages using deps.dev and bundlephobia, with specific verb 'scan dependency' and resource 'npm package'. It distinguishes from sibling tools like scan_competitor_ai_presence by scope (npm only) and purpose (safety/popularity/size).

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 asking 'is X safe/popular/small' or 'what does adding lodash cost me'. Also provides exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', giving a clear alternative.

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

A3.5/5.0
Disambiguation2/5

The 4 Codewars tools (kata, user, user_authored, user_completed) are distinct, but the 31 Pipeworx tools create real overlap: ask_pipeworx, ask_pipeworx_beta (explicitly 'currently matches ask_pipeworx exactly'), and ask_pipeworx_grounded are near-twins of the same router, and the five polymarket_* tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) have heavily overlapping opportunity-discovery purposes. discover_tools and suggest_questions also both serve as 'what can I ask' entry points. Agents will misselect between the three ask_pipeworx variants and across the prediction-market suite.

Naming Consistency2/5

Naming conventions are mixed: bare nouns (kata, user), bare verbs (forget, remember, subscribe), adjective_noun (recent_alerts, recent_changes), verb_noun (validate_claim, bet_research), and noun_verb (user_authored, user_completed) all appear. Even within the small Codewars family the prefix style is inconsistent — kata and user are bare nouns while user_authored and user_completed expect a user_ prefix, and remember/recall/forget use a different verb style than the rest of the server.

Tool Count2/5

35 tools exceeds the 25+ 'too many' threshold for a coherent server. Worse, 31 of the 35 are Pipeworx meta-research tools unrelated to the server's namesake (Codewars), so the count is drastically inflated relative to its apparent purpose — the server presents a full finance/prediction-market/research gateway while contributing only 4 tools to its advertised domain.

Completeness2/5

The actual Codewars surface has significant gaps: kata, user, user_authored, and user_completed are purely read-only, with no solution submission, attempt/training history, leaderboard access, or kata search by difficulty/language. Meanwhile the Pipeworx side is over-complete for a server not named for it, leaving the server's stated identity under-covered with no way to perform any write operation on the Codewars platform.

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