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Glama

econdata

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?

Annotations already mark it readOnly, idempotent, and non-destructive. The description adds significant behavioral context: it is a composite call, may take 5-30s on first bundlephobia measurement, degrades gracefully with partial failures, and reports sources_failed. No contradiction 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 but well-structured: it front-loads the main purpose, then usage, then return format, ecosystem scope, and failure behavior. Every sentence adds necessary information for a composite tool, and no filler words are present.

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?

With no output schema, the description must enumerate what is returned. It lists the summary block fields, per-advisory details, links, alternative versions, and sources_failed. It also notes the NPM-only scope and latency behavior, making it fully complete for decision-making.

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 has 100% coverage with clear descriptions for both 'package' and 'version'. The description mentions 'package' and 'latest version' implicitly but adds little parameter-specific meaning beyond the schema. It does not restate or contradict the schema, so the 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 a composite check for whether to add an npm package, specifying it fans out to deps.dev and bundlephobia and listing exact data points (license, advisories, bundle size). This specific verb-resource pairing distinguishes it from siblings like deep_research or validate_claim.

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...' and provides concrete examples ('is X safe / popular / small'). Also gives an exclusion: 'NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly', steering users to 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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes due to detailed descriptions. Some overlap exists, especially among research tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and prediction market tools, but boundaries are clear enough for an agent to differentiate.

Naming Consistency4/5

The majority follow a consistent verb_noun pattern with underscores (e.g., get_cpi, list_subscriptions). A few deviations exist (e.g., forget, recall, pipeworx_feedback using a prefix), but overall the pattern is predictable.

Tool Count2/5

With 34 tools, the server exceeds the typical well-scoped range of 3-15 tools. While it covers many domains, the high number makes the set feel heavy and harder to navigate, warranting a score of 2 according to calibration.

Completeness4/5

The tool set is comprehensive for its broad scope, covering economic data, company research, prediction markets, and utilities. Minor gaps exist (e.g., missing explicit GDP or stock quote tools), but the powerful ask_pipeworx meta-tool fills many gaps.

Resources