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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

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent. Description adds critical behavioral details: partial failures degrade gracefully, bundlephobia first measurement may take 5-30s, and sources_failed list. No contradictions.

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 relatively long but well-structured with clear sections. Every sentence adds value, though slight redundancy (e.g., 'composite' and 'fan out'). Still concise for amount of detail.

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?

Despite lacking output schema, description thoroughly lists return fields and covers edge cases (partial failures, timing). No additional documentation needed for agent to understand tool's behavior and output.

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%, so baseline 3. Description adds minimal extra semantics: clarifies version defaults to latest when omitted. No additional details on parameter formatting 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 is a composite check for npm packages, fanning out across deps.dev and bundlephobia. It uses specific verbs and resource, and distinguishes from sibling tools by specifying NPM-only scope.

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 tells when to use: when agent asks about safety, popularity, size, or cost of adding a package. Also notes NPM-only limitation and directs to other tools 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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters have overlapping functions: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly interchangeable, deep_research overlaps with the ask_pipeworx family, and the six Polymarket tools all analyze the same domain with subtle differences. discover_tools and suggest_questions also both serve as discovery entry points, making it difficult for an agent to confidently select the correct tool.

Naming Consistency3/5

All names are snake_case, but there is no uniform structural pattern. Verb_object names like format_currency and resolve_entity coexist with noun_phrases like entity_profile and polymarket_arbitrage, bare verbs like remember and forget, and adjective_noun forms like recent_alerts. Cluster-specific prefixes are consistent, but the overall convention is mixed.

Tool Count2/5

With 33 tools, the set is substantially over-scoped and exceeds the suggested 3-15 range. Many tools could be consolidated, such as the three ask_pipeworx variants, the six Polymarket tools, and the two formatting utilities. The broad domain justifies some size, but the count feels inflated and will burden agents with excessive choice.

Completeness3/5

The server covers many areas thoroughly: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and research tools span lookup, profiling, comparison, and verification. However, there are notable gaps: pipeworx:// citation URIs are returned but no tool explicitly fetches or reads a record by URI, and there is no direct way to manage account-level settings beyond memory. These missing operations force agents to work around limitations.