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Glama

Healthdata

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

A4.4/5.0
Behavior5/5

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

Adds substantial behavioral details beyond annotations: fans out across two sources, returns specific fields, partial failures degrade gracefully, and bundlephobia's first measurement can take 5-30s. All consistent 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.

Conciseness4/5

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

Front-loaded with purpose, but somewhat verbose with detailed return field listing and timing caveats. Every sentence adds value, but could be slightly more compact.

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 the tool's complexity (multiple data sources, partial failures), no output schema, and rich annotations, the description fully covers when to use, what it returns, edge cases (timeouts, ecosystem scope), and behavior. An agent has sufficient context.

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 coverage is 100%, so baseline is 3. The description adds minor context about scoped packages and version defaulting, but does not significantly enhance understanding beyond the schema's parameter descriptions.

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: a composite check for adding an npm package, combining data from deps.dev and bundlephobia. It uses a specific verb (scan) and resource (dependency), and distinguishes from sibling tools by its unique scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also mentions NPM-only scope and partial failure behavior. Lacks explicit when-not-to-use, but context is clear.

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

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but a few near-duplicates exist: ask_pipeworx and ask_pipeworx_beta are functionally identical right now, and ai_visibility_check vs scan_competitor_ai_presence overlap. The polymarket sub-family also has multiple edge/fill tools that could be confused.

Naming Consistency3/5

The naming is varied but readable. Many tools follow verb_noun (ask_pipeworx, resolve_entity, search_within, subscribe), yet several are noun phrases (entity_profile, recent_changes, polymarket_edges, bet_research, pipeworx_feedback). There's no single coherent pattern, but the mixture is not chaotic.

Tool Count2/5

At 33 tools, the server is oversized for typical MCP coherence. The breadth is broad (prediction markets, healthcare datasets, memory, subscriptions), but such a large count forces agents to filter through many utilities (suggest_questions, discover_tools, generate_llms_txt) that could be consolidated or hidden.

Completeness4/5

The domain—data querying and analysis—is well covered: universal routing (ask_pipeworx), grounded verification, deep research, entity resolution, dataset metadata, subscriptions, prediction-market edge checks, and memory tools. Minor gaps exist (e.g., no direct health-care-specific analytics batch or file download), but no major dead ends for core workflows.

Resources