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Glama

Rawg

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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds behavioral details: fans out across two services, partial failure handling, bundlephobia timing (5-30s), and listing failed sources. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but front-loaded with the core purpose. Some verbosity in enumerating return fields, but every sentence adds value. Could be slightly more structured (e.g., bullet points) but remains clear.

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 fully explains return structure (summary block, per-advisory details, links, alternative versions). Covers partial failures, ecosystem scope, and timing. Complete for a tool with rich annotations.

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% with clear descriptions. The description adds value by mentioning scoped packages ('@types/node') and explicitly stating version defaults to latest. While the schema already covers basics, the description reinforces and adds context.

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 performs a composite check for npm packages, combining deps.dev and bundlephobia data. It uses specific verbs ('check', 'fans out') and resources ('npm package'), and the function is distinctive among siblings (none other do this composite check).

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 specifies when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also states limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', and handles partial failures gracefully.

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

All Rawg toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx, bet_research overlaps heavily with polymarket_edges, and entity_profile/recent_changes/compare_entities/validate_claim all pull from the same SEC/news fundamentals space. The memory trio and game lookup tools are distinct, but too many other tools could be confused for one another.

Naming Consistency3/5

The surface is uniformly snake_case and has some strong families (list_*, polymarket_*, ask_pipeworx_*), but it mixes verb-led names (search_games, remember, validate_claim) with noun-led names (entity_profile, polymarket_arbitrage, pipeworx_trending) and brand-style names like ask_pipeworx. There is a pattern, but it is not a single consistent one.

Tool Count2/5

35 tools is a heavy surface, and the set feels sprawling rather than focused: a four-tool RAWG game submodule sits alongside a general-purpose research platform, Polymarket edge tooling, memory helpers, subscriptions, feedback, and meta-utilities. Many of these could have been consolidated or split into separate servers.

Completeness3/5

As a general data-research and prediction-market platform the coverage is strong, with lookup, grounded verification, entity resolution, research fan-out, and subscription flows. But for a server named Rawg, the game domain is thinly covered with only search/get/list tools and no game-detail enrichment or broader browsing surface, leaving the core domain feeling like an afterthought.

Resources