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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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, open-world. Description adds critical behavioral detail: partial failure handling, bundlephobia first measurement latency (5-30s), and listing failed sources. 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?

Long but densely informative; every sentence adds value. Could be slightly more concise but front-loaded effectively with purpose and use cases.

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?

No output schema, but description thoroughly covers return block structure, per-advisory details, links, alternative versions, and error behavior, making it complete for an agent to understand results.

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 parameter descriptions. Description adds nuance (default version to latest, scoped packages accepted) but doesn't fundamentally extend beyond what schema already provides.

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 specifies a composite npm package evaluation (license, advisories, bundle size) and distinguishes from siblings by confining to NPM ecosystem v1, with clear fallback alternatives.

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 (agent asks about safety/popularity/size or cost) and provides exclusion criteria (non-NPM ecosystems go to deps.dev:version directly).

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

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but the cluster of Pipeworx tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar, causing potential confusion. The memory tools (remember, recall, forget) also add some overlap.

Naming Consistency3/5

All tool names use lowercase with underscores, which is consistent. However, the similar Pipeworx tools have confusingly similar names (ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta), and the naming does not clearly distinguish their differences.

Tool Count2/5

With 34 tools, the set is too large for a server ostensibly focused on Yu-Gi-Oh! cards. The majority of tools are unrelated domain-agnostic data tools, making the count feel bloated and unfocused.

Completeness2/5

The Yu-Gi-Oh! card tools are limited to lookup and search, missing obvious operations like creating or updating cards. The unrelated data tools, while many, do not form a coherent set for a single purpose, leaving gaps in both directions.