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Glama

Open Meteo

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

A4.8/5.0
Behavior5/5

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

Beyond idempotent/readOnly annotations, the description adds behavioral traits: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. 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?

Description is detailed but well-structured, front-loading the composite nature and use case. Slightly verbose with examples, but every sentence adds context. Could be slightly more concise, but overall effective.

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?

Despite no output schema, the description explicitly enumerates the return fields (is_latest, license, advisory_count, bundle sizes, etc.) and explains partial failure behavior. This makes the tool fully understandable for invocation.

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 description coverage is 100% for both parameters. The description adds value by clarifying that scoped packages are accepted for the 'package' param and that 'version' defaults to latest when omitted, which aids understanding beyond 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 is a composite check for evaluating npm packages, listing specific dimensions (license, advisories, version history, bundle size). It distinguishes from sibling tools by specifying NPM-only scope and mentioning alternative direct calls 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?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also notes NPM-only in v1 and directs other ecosystems to deps.dev:version directly.

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

All Open Meteo toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx vs ask_pipeworx_grounded and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research). While some tools are clearly distinct (e.g., geocode vs forecast), the high number of similar tools increases the risk of agent misselection.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use a consistent verb_noun pattern (e.g., ask_pipeworx, resolve_entity), while others have no prefix (remember, recall) or use a domain prefix (polymarket_arbitrage, pipeworx_feedback). The mix of styles and lack of a unified pattern reduces predictability.

Tool Count2/5

With 33 tools, the set is too large for a focused server, especially given the name 'Open Meteo' which implies weather tools only. Many tools are redundant or cover vastly different domains, making the count feel bloated and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers an impressively wide range of capabilities: weather data, SEC filings, Polymarket analysis, memory management, and more. For the actual scope of data querying and analysis, there are few obvious gaps (e.g., no direct database query tool beyond ask_pipeworx). However, the completeness relative to the implied weather domain is poor.

Resources