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Glama

Datos Cl

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?

Discloses behavioral traits beyond annotations: returns a summary block with specific fields, per-advisory details, and links; partial failures degrade gracefully; bundlephobia's first measurement can take 5-30s and sources_failed will list it. No contradiction with readOnly/idempotent hints.

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?

Although longer than typical, every sentence is information-dense: composite action, use cases, return fields, ecosystem limits, failure behavior. Front-loaded with purpose, no 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?

Given no output schema, the description fully explains return fields, ecosystem scope, and timeout/failure behavior. It includes enough context for an agent to invoke correctly and interpret results.

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?

Schema covers 100% of parameters with clear descriptions (package name, optional version defaulting to latest). The description adds no new parameter meaning 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?

Clearly states it is a composite 'should I add this npm package to my project' check that fans out to deps.dev and bundlephobia. Specific verb ('check'), resource ('npm package'), and scope, distinguishing it from package_list/search/show siblings.

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 says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and notes that non-NPM ecosystems should use deps.dev:version directly. This provides clear when-to-use 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

A3.6/5.0
Disambiguation4/5

Tools are generally distinct in purpose, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research. Descriptions clearly differentiate them, but an agent might still need to carefully choose.

Naming Consistency4/5

Most tool names follow a consistent snake_case pattern with clear prefixes (e.g., pipeworx_*, polymarket_*). A few names like compare_entities and validate_claim break the pattern, but overall it is predictable.

Tool Count2/5

With 40 tools, the server is overly large for a single coherent set. Many tools are meta or auxiliary, but the count exceeds the 'too many' threshold, making it hard for agents to navigate.

Completeness4/5

The tool set covers data retrieval, Chilean open data, Polymarket, memory, and monitoring comprehensively. Minor gaps exist (e.g., no direct trading for Polymarket), but the surface is complete for the stated purpose.

Resources