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Glama

Meteors

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

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnly, idempotent hints, but the description adds valuable behavioral details: fans out across deps.dev and bundlephobia, returns specific fields, partial failures degrade gracefully, and Bundlephobia timing issue. This adds significant context beyond 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?

The description is detailed but front-loaded with core purpose. Each sentence adds value, though slightly long. Could be tightened without losing information.

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 lists all returned fields in parentheses, covers partial failures, timing, and ecosystem scope. It is complete for a composite tool.

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 coverage is 100% but description adds context: package name is required, accepts scoped packages, version defaults to latest. This adds value beyond schema, but schema already documents parameters well.

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: a composite check for adding an npm package, covering licenses, advisories, version history, and bundle size. It distinguishes from siblings by focusing on npm ecosystem and mentioning alternatives 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use (e.g., 'is X safe/popular/small') and provides limitations (NPM only, partial failures, timing). It could be improved by explicitly saying when not to use (e.g., for non-npm packages).

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

Several tools serve nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly functionally identical right now, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap as query/discovery entry points. The six prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together for agents looking to find or size a trade.

Naming Consistency2/5

Naming is inconsistent: some tools use get_/list_/scan_ prefixes while others are bare nouns (polymarket_edges, entity_profile, recent_alerts), and the Pipeworx prefix appears only on some tools (pipeworx_feedback, pipeworx_trending) while equivalent tools are named ask_pipeworx or deep_research. Verb styles vary between imperative (validate_claim, resolve_entity) and descriptive (bet_research, polymarket_arbitrage).

Tool Count2/5

34 tools is heavy for a server whose name (Meteors) covers only 3 of them. The bulk belongs to unrelated domains — Pipeworx data access, prediction markets, memory, npm scanning, llms.txt generation — making the surface feel like an unfocused grab bag rather than a deliberate product. Many of the 34 tools could be consolidated (e.g., the three near-identical ask_pipeworx variants).

Completeness2/5

The server has no coherent domain to assess completeness against: meteor data is limited to three lookups with no management/CRUD, memory tools have save/recall/delete but no update, and the Pipeworx surface lacks obvious editing or administrative operations beyond subscriptions. The prediction-market toolset is thorough, but it sits awkwardly beside unrelated utilities, leaving the overall tool surface feeling incomplete for any single stated purpose.

Resources