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Glama

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 indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable context: external APIs are called (deps.dev, bundlephobia), bundlephobia's first measurement can take 5-30s and may time out with sources_failed, and the return format includes summary, advisories, links, and alternative versions. No contradiction.

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 that front-loads the core purpose. It is informative but slightly lengthy due to comprehensive detail. Every sentence serves a purpose, but could be trimmed marginally. Still well-structured and efficient.

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 the complexity (multiple data sources, partial failure, no output schema), the description covers return value structure (summary block fields, per-advisory, links, alternative versions), limitations (NPM only, bundlephobia latency), and graceful degradation. Fully adequate for an agent to understand behavior.

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 coverage is 100% with clear descriptions for 'package' (npm package name, scoped accepted) and 'version' (optional, defaults to latest). The description mentions defaulting to latest but adds little beyond schema. Baseline 3 is appropriate; no extra semantic richness.

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 starts with a specific verb-resource combination ('Composite check ... in ONE call') and clearly states the tool scans npm packages for safety, popularity, size, etc. It distinguishes from other tools by explicitly noting NPM-only scope and referencing sibling tools for other ecosystems (deps.dev:version directly).

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?

The description explicitly states when to use: 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. It also provides exclusions ('NPM ecosystem only in v1') and alternatives ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), plus behavior under partial failures.

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

A3.6/5.0
Disambiguation2/5

Several tools blur together: ask_pipeworx_beta currently duplicates ask_pipeworx exactly, ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to data sources, and the prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research) have fuzzy boundaries. The detailed descriptions help, but an agent can easily misselect among them.

Naming Consistency3/5

All names are lowercase snake_case and the rxnorm_, polymarket_, and pipeworx_ prefixes create readable groupings, but the set mixes imperative verb-object names (validate_claim, list_subscriptions), noun-phrase names (entity_profile, recent_alerts), and bare verbs (remember, forget). It is readable but not a predictable uniform naming convention.

Tool Count2/5

35 tools is in the too-many band for a coherent server, and the Rxnorm identity makes it worse: only 4 tools are actually RxNorm-specific while 31 are unrelated Pipeworx, prediction-market, memory, and utility tools. A focused RxNorm server would need a fraction of this surface.

Completeness3/5

The four rxnorm_* tools plus resolve_entity cover the main RxNorm lookup flow (search, properties, related, NDC), so the core is not broken. But the set lacks a reverse NDC-to-concept lookup and the 31 non-RxNorm tools do not complete any single coherent domain, leaving notable gaps relative to the server's stated purpose.