Skip to main content
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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context beyond annotations: composite fan-out, graceful degradation of partial failures, bundlephobia's first measurement delay (5-30s), and the sources_failed field. No contradictions 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.

Conciseness5/5

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

The description is dense yet compact, front-loading the core purpose and then providing use cases, output fields, and limitations in a logical order. Every sentence carries useful information without redundancy.

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, the description enumerates the exact summary fields returned (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), plus per-advisory detail and alternative versions. It covers ecosystem scope, performance caveats, and failure behavior, making it complete for a composite tool of this complexity.

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 description coverage is 100%, so the baseline is 3. The description adds extra meaning: scoped packages are accepted ('@types/node') and version defaults to latest when omitted. It also clarifies the npm-only ecosystem constraint, which is not fully captured by the generic 'npm package name' schema description.

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: 'should I add this npm package to my project' in one call, then names the fan-out sources (deps.dev, bundlephobia). This clearly identifies the tool's verb, resource, and scope, distinguishing it from all sibling tools.

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 states when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides a clear alternative for non-NPM ecosystems: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly'. No ambiguity about when to invoke this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation3/5

Some tools have distinct purposes (e.g., color tools, memory tools), but many data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) overlap significantly, making it hard for an agent to choose the right one without deep knowledge of their nuances.

Naming Consistency4/5

Most tool names use snake_case with a verb_noun pattern (e.g., convert_color, identify_color, resolve_entity), but there is inconsistency in prefixes: some use 'ask_pipeworx', others 'pipeworx_', 'polymarket_', or 'scan_'. Overall, still readable and predictable.

Tool Count3/5

33 tools is on the high side for a single server, especially one named 'colorapi' which misleadingly suggests a focus on color only. The tool count is borderline appropriate for the actual broad data integration scope, but the mismatch with the server name is problematic.

Completeness4/5

The tool set covers a wide range of data sources and operations (SEC, FDA, patents, news, Polymarket, memory, subscriptions), with few obvious gaps. Minor lacks: no direct tool for updating stored memories or handling non-US companies, but overall comprehensive.