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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

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, but the description adds substantial behavioral context beyond that: composite fan-out across multiple services, graceful partial failures, potential 5-30s latency on first bundlephobia measurement, and the sources_failed field. This is exactly the kind of behavior an agent needs to know.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is dense but every sentence earns its place: purpose, usage triggers, return structure, ecosystem limitation, and failure behavior. It is front-loaded with the most important information and contains no redundant phrasing.

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 there is no output schema, the description compensates by enumerating the exact return fields (is_latest, license, advisory_count, bundle_kb_min, etc.), per-advisory detail, links, and alternative versions. It also covers latency, partial failures, and ecosystem scope, providing a complete mental model for invocation.

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 input schema already covers both parameters 100% with clear descriptions (package name, scoped packages accepted, version defaults to latest). The description reinforces the npm ecosystem and mentions output fields, but adds no parameter semantics beyond the schema, so the schema-coverage baseline of 3 applies.

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 opens with a specific composite check purpose ('should I add this npm package to my project') and names its data sources (deps.dev, bundlephobia). It clearly scopes to npm and distinguishes itself from generic dependency tools by mentioning that other ecosystems use deps.dev 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?

Explicitly provides usage triggers with example queries: 'is X safe / popular / small' or 'what does adding lodash cost me'. It also gives an exclusion rule, stating PyPI/Maven/Cargo/Go fall under deps.dev:version directly, which helps the agent choose alternatives.

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
Disambiguation4/5

Most tools are carefully delineated with explicit use-case guidance; ask_pipeworx, ask_pipeworx_grounded, and deep_research are clearly separated by depth and grounding. The main weak spots are ask_pipeworx_beta, which is a current functional duplicate of ask_pipeworx, and the several prediction-market/company-research tools that still require careful reading to pick correctly.

Naming Consistency4/5

Names are uniformly snake_case and mostly command-like, with coherent families such as polymarket_*, ask_pipeworx_*, get_art*, subscribe/unsubscribe, and remember/recall/forget. The pattern is not strictly verb_noun throughout, since noun phrases like entity_profile, pipeworx_trending, and recent_changes appear, but the inconsistency is minor and readable.

Tool Count2/5

35 tools is well past the 25+ threshold for a single MCP server, and the set bundles Art Institute lookups, Pipeworx data research, prediction-market analysis, memory, subscriptions, and website utilities into one place. Many tools earn their keep individually, but the overall toolbox feels bloated and poorly scoped.

Completeness3/5

The Pipeworx, memory, and subscription subgroups have decent lifecycle coverage: remember/recall/forget and subscribe/unsubscribe/listsubscriptions/recent_alerts form coherent loops. But relative to the 'artic' server name, the Art Institute surface is thin—there is no artist search and no exhibition-detail tool—and the unrelated embedded domains prevent the set from feeling complete for any one clear purpose.