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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?

The description adds significant behavioral context beyond annotations: it details that it fans out to two services, handles partial failures gracefully, and notes that bundlephobia's first measurement can take 5-30 seconds. It also mentions that sources_failed will list timeouts. No contradictions with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint).

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 reasonably concise given the complexity of the tool. It is front-loaded with the main purpose and use case, then details return fields, limitations, and error handling. Each sentence adds value, though it could be slightly shorter by omitting the list of return fields since they are detailed later.

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 an output schema, the description thoroughly lists all return fields (summary block fields, per-advisory detail, links, alternative versions). It covers ecosystem limitations, partial failures, and timing behavior. For a two-parameter tool, this provides complete context for an agent to use correctly.

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?

The input schema already describes both parameters with 100% coverage (package name, version). The description adds value by clarifying that scoped packages (e.g., '@types/node') are accepted and that version defaults to the latest. This supplements the schema without redundancy.

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 as a composite check for evaluating npm packages, specifying it fans out to deps.dev and bundlephobia. It uses specific verbs like 'scan' and refers to the exact question it answers ('should I add this npm package to my project'). It distinguishes itself from siblings by focusing solely on npm packages and mentioning its composite nature.

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?

The description explicitly tells when to use the tool: when an agent asks about safety, popularity, or size of an npm package. It also specifies limitations: NPM ecosystem only for v1, and references deps.dev:version for other ecosystems. This provides clear guidance on when not to use it and 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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed routing guidance, but the three ask_pipeworx variants and overlapping company-focused tools (entity_profile vs compare_entities vs recent_changes) create some ambiguity. The descriptions are thorough enough that an agent can usually select correctly.

Naming Consistency4/5

All names are snake_case and mostly descriptive, but they mix verb-first (ask_pipeworx, discover_tools), noun-first (entity_profile, gold_price), and domain-prefixed (polymarket_*, pipeworx_*) patterns. The style is consistent enough to be predictable, though not uniform verb_noun.

Tool Count2/5

34 tools is well past the 25-tool threshold for "too many," and the server name (Nbp Pl) suggests a narrow Polish-bank scope while most tools cover unrelated domains like prediction markets, dependency scanning, and AI visibility. The breadth makes the set feel over-stuffed and unfocused.

Completeness4/5

The toolset covers a remarkably complete data-research workflow: general querying, grounded answers, deep research, entity resolution, profiles, comparisons, claim validation, change feeds, discovery, subscriptions, and memory. Minor gaps like subscription updating or a direct source catalog exist, but agents can work around them.