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Glama

Itis

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

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint. Description adds that partial failures degrade gracefully, bundlephobia first measurement may take 5-30s, and sources_failed will list timeouts. This is useful 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?

Description is detailed but every sentence adds value: composite nature, sources, use cases, ecosystem info, partial failure handling. Slightly long but not wasteful. Could be more structured (e.g., bullet points) but still 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?

For a read-only composite tool with no output schema, the description thoroughly explains return fields (summary block, per-advisory, links, alternatives) and behavior (partial failures). Completely prepares the agent 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 covers both parameters with descriptions (100% coverage). Description adds that scoped packages ('@types/node') are accepted for 'package', and 'version' defaults to latest published when omitted. Provides extra context 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?

Description clearly states it's a composite check for npm package evaluation, covering license, advisories, version history, and bundle size. It distinguishes from sibling tools which are unrelated (e.g., AI agents, polymarket). Verb 'scan' + resource 'dependency' is specific.

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 states when to use: when agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. Also notes ecosystem limitations (NPM only in v1) and alternative (deps.dev:version for other ecosystems).

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

A3.9/5.0
Disambiguation2/5

Several tools occupy overlapping roles: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded/deep_research/validate_claim all provide grounded answering, and bet_research/polymarket_edges/polymarket_arbitrage all surface betting opportunities. The descriptions are detailed, but the boundaries between routers and research modes are fuzzy enough that an agent could easily select the wrong one. The taxonomy, memory, and subscription clusters are distinct, but they are drowned out by the overlapping meta-tools.

Naming Consistency3/5

All names use snake_case, which provides some visual consistency, but the verb_noun pattern is not consistently applied: search_taxa/get_hierarchy are clean verb_noun, while deep_research, entity_profile, polymarket_edges, and bet_research are noun-ish or reversed patterns. There is good family-level consistency within ask_pipeworx_* and polymarket_*, but the overall set mixes conventions and requires reading descriptions to infer what each tool does.

Tool Count2/5

34 tools is well over the 25-tool threshold and reflects a sprawling multi-domain server spanning taxonomy, structured-data lookup, prediction markets, subscriptions, memory, and AI visibility. Each cluster may be individually reasonable, but as a single MCP surface it is too heavy and forces agents to filter through many irrelevant tools.

Completeness4/5

For the broad data-research and prediction-market purpose, the surface is fairly complete: it covers routing, grounded answers, deep multi-source research, entity profiles, comparisons, claim validation, entity resolution, subscriptions, alerts, memory, and market edge/fill checks. Minor gaps exist—no direct web-search tool, the beta router adds no current behavior, and some patent endpoints soft-fail—but agents can generally work around them.

Resources