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Glama

Pirate Weather

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, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral details: composite fan-out to multiple services, partial failure degradation, bundlephobia's 5-30s first measurement, and that sources_failed lists timeouts. No contradictions.

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 well-structured, starting with the core functionality, then usage, return structure, and caveats. Every sentence provides essential information without redundancy. It is appropriately sized for the complexity of the tool.

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?

With no output schema, the description fully explains the return format (summary block fields, per-advisory detail, links, recent versions) and error behavior. It also covers ecosystem scope and fallback alternatives, making it complete for a tool of this complexity.

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 clear parameter descriptions. The description does not add significant meaning beyond the schema, only implying defaults and scoped packages, which are already covered. Baseline 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?

The description clearly states it is a composite check for deciding whether to add an npm package, covering license, advisories, bundle size, and tree-shake support. It distinguishes itself from sibling tools by focusing on npm dependency scanning, explicitly noting the ecosystem limitation and alternatives.

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?

The description provides explicit usage guidance with example queries and states when to use (e.g., 'is X safe / popular / small') and when not to use (other ecosystems like PyPI, Maven, etc.), including direct alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Pirate Weather toolsInspect
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TDQS

B3.1/5.0
Disambiguation3/5

Tools are mostly distinct in purpose but some overlaps exist, e.g., multiple ask_pipeworx variants and several polymarket analysis tools. Detailed descriptions help, but the sheer variety and similar intent of some tools could confuse agents.

Naming Consistency2/5

Naming is highly inconsistent: snake_case (ai_visibility_check), single words (forecast), action_noun (bet_research), prefixes (polymarket_, pipeworx_), and descriptive phrases (scan_competitor_ai_presence). No uniform pattern.

Tool Count2/5

33 tools is too many for a server named 'Pirate Weather' that only has two weather-specific tools. The tool count feels inflated with many meta-tools and unrelated domains, exceeding a coherent scope.

Completeness2/5

Coverage is incomplete for the implied weather focus (only two tools). Other domains like prediction markets are better covered, but overall the surface is a mix of partial offerings with clear gaps (e.g., no update/delete for subscriptions).

Resources