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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?

The description discloses multiple behavioral aspects beyond the annotations: it returns a structured summary block, degrades gracefully on partial failures, notes that bundlephobia's first measurement can take 5-30s, and that sources_failed will list timeouts. This adds valuable operational context not present in the readOnly/openWorld/idempotent 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 long but front-loaded with the primary purpose and densely packed with essential information: sources, output fields, usage triggers, ecosystem limitation, and failure behavior. Every sentence contributes value, though a slight trim for readability would make it tighter.

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?

The description fully covers the tool's functionality, output format, edge cases (timeouts, partial failures), ecosystem restrictions, and alternatives. Since there is no output schema, the description successfully carries the responsibility of explaining return values and 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?

The schema already provides 100% coverage for both parameters, including package name details and version defaulting. The description adds minimal additional parameter semantics beyond confirming the NPM-ecosystem scope, so the baseline score of 3 is appropriate.

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 with a specific verb and resource: a composite 'should I add this npm package to my project' check that fans out across deps.dev and bundlephobia. It distinguishes itself from sibling tools by describing a unique, specialized function not present in any sibling names.

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 describes when to use the tool ('Use whenever an agent asks...') and also provides exclusions for non-NPM ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This clear guidance helps agents choose the correct tool.

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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Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation1/5

The set contains multiple near-identical query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and five overlapping prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) that an agent could easily confuse. The Studio Ghibli tools are clear enough, but they are drowned out by a large unrelated cluster with fuzzy boundaries.

Naming Consistency2/5

There are small internally consistent clusters (polymarket_* tools, ask_pipeworx variants, singular/plural Ghibli resource pairs), but the overall set mixes simple nouns (film, person, location), imperative verbs (remember, forget, recall), and descriptive compound names (ai_visibility_check, generate_llms_txt, scan_competitor_ai_presence). The species tool is 'species'/'species_one' while every other resource uses bare singular for the single-item fetch, breaking the otherwise predictable Ghibli pattern.

Tool Count1/5

A server named 'Studio Ghibli' exposes 41 tools, but only 10 of them relate to Ghibli content; the other 31 are an unrelated general-purpose data, prediction-market, memory, and subscription toolkit. This is an extreme mismatch between the apparent purpose and the actual tool surface.

Completeness4/5

For the Ghibli data domain itself, the surface is solid: films, people, locations, vehicles, and species all have list and single-item lookup, plus cross-links between entities. Minor gaps exist, such as no search or filter capability and no way to fetch films by director or year, but the core read-only catalog is well covered.