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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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds behavioral details: partial failures degrade gracefully, bundlephobia's first measurement may take 5-30s, sources_failed field. No contradiction with 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?

Description is dense but front-loaded with purpose and usage. Every sentence contributes value, though length could be slightly trimmed for conciseness.

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 no output schema, description lists returned fields, explains partial failure behavior, and covers key behavioral aspects. For a composite tool with two params, it is highly complete.

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 descriptions for both parameters. Description adds nuance: scoped packages accepted for 'package', default to latest version for 'version'. Adds value 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 uses specific verbs ('scan', 'check') and resource ('npm package dependency') and clearly distinguishes from siblings by focusing on npm ecosystem and composite checks across multiple sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use whenever an agent asks 'is X safe / popular / small''. Provides ecosystem limitation: 'NPM ecosystem only in v1' and alternative tools for other ecosystems. Lacks explicit when-not-to-use but guidance is clear.

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
Disambiguation3/5

Many tools overlap in purpose, especially the Pipeworx data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, etc.). Descriptions help differentiate, but an agent may still struggle to choose the correct one.

Naming Consistency4/5

Most tool names use snake_case and follow a verb_noun pattern (e.g., ask_pipeworx, resolve_entity, validate_claim). Some deviations exist (e.g., entity_profile, cheat_sheet) but overall the pattern is predictable.

Tool Count2/5

34 tools is excessive for a server named 'Owasp', as only 4 are directly OWASP-related (asvs_chapters, asvs_requirements, cheat_sheet, top10). The remaining 30 tools are for general data querying and betting, making the surface feel bloated and unfocused.

Completeness3/5

The OWASP domain is partially covered with ASVS requirements, cheat sheets, and Top 10 lists. However, notable gaps exist, such as the OWASP Testing Guide, Software Assurance Maturity Model (SAMM), or risk assessment tools. The Pipeworx tools are comprehensive but not relevant to OWASP.