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Glama

Data Denver

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe read-only operation. Description adds critical behavioral context: fans out across multiple services, potential 5-30s delay for bundlephobia first measurement, graceful degradation with sources_failed field. 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.

Conciseness4/5

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

Single dense paragraph, front-loaded with purpose. Every sentence adds value, but could be slightly restructured for readability. Efficient use of words given the complexity.

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?

No output schema, so description must explain returns. It does so thoroughly: summary block fields, per-advisory details, links, alternative versions. Also covers error handling and ecosystem scope. Nothing missing for this tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by noting that version defaults to latest and that scoped packages are accepted. While schema already describes parameters, the description provides additional usage context.

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?

Description clearly states the tool's purpose: a composite check for npm packages using deps.dev and bundlephobia. It specifies the verb (scan/check), the resource (npm packages), and distinguishes from siblings by being a multi-source composite call.

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: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', providing clear when-not-to-use guidance.

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
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta is currently exactly the same), and scan_competitor_ai_presence is a multi-entity wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong variant when a simple lookup is needed.

Naming Consistency3/5

Tool names mix verb-first patterns (ask_pipeworx, resolve_entity, scan_dependency, validate_claim) with noun-first patterns (denver_layers, entity_profile, recent_changes, pipeworx_trending, polymarket_edges). There are clear families (ask_pipeworx_*, denver_*, polymarket_*, pipeworx_*) but no single consistent verb_noun convention across the set.

Tool Count2/5

34 tools is a large surface for one server, exceeding the 25-tool threshold where coherence starts to degrade. Many tools are meta-routers or near-duplicates (ask_pipeworx family), and the mix of general data access, Denver-specific queries, prediction-market analysis, memory, and subscriptions feels sprawling rather than tightly scoped.

Completeness4/5

The tool surface covers the apparent domain well: universal data lookup, grounded evidence, deep research, entity resolution, company profiles, comparisons, claim validation, AI visibility, dependency scanning, prediction-market analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct update tool for subscriptions, no way to inspect the full 5,798-tool catalog locally without routing through ask_pipeworx), but agents can accomplish most workflows without dead ends.

Resources