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Glama

airports

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?

Even though annotations already declare readOnlyHint and idempotentHint, the description adds valuable behavioral detail: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and the sources_failed field will list timeout issues while the rest still returns. This goes beyond the 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?

The description is information-dense but every sentence earns its place: main purpose, fan-out targets, exact use cases, ecosystem limitation, partial failure behavior, latency, and return fields. It is front-loaded with the core purpose and contains no fluff.

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 there is no output schema, the description thoroughly lists the full return structure (summary block fields, per-advisory detail, links, alternative versions) and covers partial failure behavior, latency, and ecosystem constraints. This makes it complete for an agent to invoke correctly.

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 description coverage is 100% for both parameters, and the schema already explains package name (including scoped packages) and version defaulting. The description does not add parameter-specific meaning beyond what the schema provides, so the 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?

The description clearly states the tool's purpose with a specific verb ("check") and resource (npm package), and distinguishes it from siblings by describing the composite fan-out across deps.dev and bundlephobia. It answers the exact question "should I add this npm package to my project".

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 when-to-use guidance is given: "Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'". It also excludes non-NPM ecosystems and directs the user to deps.dev:version directly for PyPI/Maven/Cargo/Go, providing clear alternatives.

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

Many tools have distinct purposes, but the three 'ask_pipeworx' variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are highly similar and likely cause confusion. Additionally, the toolset mixes airport-specific tools with unrelated financial and research tools, creating ambiguity about when to use which.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use descriptive snake_case (ai_visibility_check, ask_pipeworx), others are single verbs (remember, forget, recall), and some include brand names (pipeworx_feedback). No clear pattern emerges across the 34 tools.

Tool Count1/5

34 tools is excessive for a server named 'airports'—only 3 tools directly relate to airports (search_airports, get_airport, calculate_distance). The majority are unrelated utilities (financial, prediction markets, memory), making the scope far too broad and unfocused.

Completeness1/5

The server severely lacks completeness for its stated airport domain: there are no tools for flights, airlines, runways, or real-time data. The other included domains (e.g., financial, prediction markets) are also incomplete, with e.g., only partial coverage of company data and no update/delete operations.

Resources