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Glama

Data Calgary

by io.github.pipeworx-io

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

A4.7/5.0
Behavior5/5

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

Annotations cover read-only and idempotent hints, but the description adds valuable behavioural context: it fans out across two services, partial failures degrade gracefully, and a 5-30s timing risk for bundlephobia's first measurement. This goes well beyond what annotations convey.

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 adds value: it front-loads the core purpose, lists key outputs, notes ecosystem limitations, and explains failure handling. No pointless filler.

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?

For a composite tool with no output schema, the description covers all essential aspects: what it does, when to use it, what it returns, failure modes, and ecosystem scope. It is fully adequate for an agent to select and invoke correctly.

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 schema already provides 100% coverage with descriptions for both parameters (package name and version). The description adds minimal extra semantics (e.g., example 'lodash'), and does not clarify defaults or formats beyond the schema, so baseline 3 is appropriate.

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 this is a composite 'should I add this npm package' check, naming both deps.dev and bundlephobia. It is specific about the resource and scope, and distinguishes itself from sibling tools by being the only npm-focused dependency scanning tool.

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?

Explicit usage guidance is given: 'Use whenever an agent asks "is X safe / popular / small"...' It also provides a clear exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly,' effectively naming the alternative.

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

A4.3/5.0
Disambiguation4/5

Most tools have distinct purposes, but there are overlapping families (ask_pipeworx variants, company research tools) that could cause confusion. Descriptions help differentiate, but an agent might misselect without careful reading.

Naming Consistency4/5

Tools follow snake_case with a verb+noun pattern, but some names are less clear (e.g., 'recall', 'remember' are verbs alone). Overall consistent enough, with minor deviations.

Tool Count4/5

33 tools is on the higher side, but the server covers a wide domain (data retrieval, prediction markets, monitoring). Each tool has a clear purpose, so the count feels appropriate rather than excessive.

Completeness5/5

The tool surface is remarkably complete: querying, comparisons, monitoring, alerts, memory, arbitrage, edge tracking. There are no obvious gaps for the intended data analytics and prediction market use case.

Resources