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Glama

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all occupy adjacent lookup/discovery territory. The four Codewars tools are clear, but the broader set is genuinely hard to navigate.

Naming Consistency3/5

Most names follow a readable snake_case verb_noun style with useful prefixes like polymarket_ and user_, but there are one-word outliers (kata, user, forget, recall, remember) and inconsistent phrasing such as ai_visibility_check versus scan_competitor_ai_presence. The naming is mostly predictable but not uniform.

Tool Count1/5

35 tools is already heavy, and the server is named Codewars while only 4 of the 35 tools relate to Codewars. The remaining 31 tools belong to a completely different Pipeworx research/prediction-market/brand-visibility product, making the count both excessive and fundamentally mismatched to the server's stated identity.

Completeness1/5

For a Codewars server, the surface is severely incomplete: you can fetch a single kata and a user's profile, authored list, and completed list, but there is no search, no kata listing by rank/tag, no solution submission or training workflow, and no way to manage authored kata. The unrelated Pipeworx tools do not fill these gaps; they point at a different domain entirely.

Resources