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Glama

microlink

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses behavior like partial failure degradation ('sources_failed will list it if it times out'), the 5-30s first-measurement delay for bundlephobia, and the exact return shape. This gives agents realistic expectations for latency and error handling.

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 not verbose; every sentence adds value: purpose, usage, output fields, ecosystem scope, and failure behavior. It's appropriately sized for a composite tool and front-loaded with the primary purpose.

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?

The absence of an output schema is compensated by listing all return fields explicitly. The description covers return values, error handling, timing, and ecosystem limitations, making it complete for a read-only npm dependency evaluation tool.

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 input schema already provides full descriptions for both parameters (package name and version). The description adds minimal additional parameter-specific semantics but reinforces the default behavior (latest version) without contradicting the schema, so the baseline of 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 the tool's purpose as a composite npm package evaluation check, enumerating specific data sources (deps.dev and bundlephobia) and the aspects it covers (license, advisories, version history, bundle size, dependency count, ESM/tree-shake support). This makes it highly distinguishable from 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 specifies ecosystem limits ('NPM ecosystem only in v1') and directs other ecosystems to deps.dev:version directly, providing clear exclusions.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed usage guidance, but the several query entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) overlap conceptually and require careful reading to select correctly. Notably, ask_pipeworx_beta currently behaves identically to ask_pipeworx, which could cause confusion.

Naming Consistency3/5

Tool names mix verb-first (remember, resolve_entity) and noun-first (entity_profile, deep_research) patterns, with some using prefixes like 'polymarket_' or 'ask_pipeworx'. While all are snake_case and readable, the lack of a single consistent convention makes the set feel less coherent than it could be.

Tool Count3/5

33 tools is on the high end for a single server, though the broad domain (data retrieval, prediction markets, memory, subscriptions, web scraping) justifies much of the sprawl. Still, the count borders on heavy, and some tools could potentially be consolidated (e.g., the ask_pipeworx variants).

Completeness4/5

The toolset provides comprehensive coverage for its core data querying and analysis domain, with lifecycle coverage for memory and subscriptions. Minor gaps exist, such as no generic 'fetch page content' tool despite having get_metadata and take_screenshot, but these do not undermine the primary functionality.

Resources