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Glama

Data Detroit

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?

Beyond annotations (readOnly, idempotent), describes partial failure handling, timing (5-30s for bundlephobia first measurement), and graceful degradation with sources_failed field.

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?

Description is comprehensive but front-loaded with purpose. Every sentence provides useful detail, though length could be slightly reduced without loss.

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, but description fully enumerates return fields (summary, advisory detail, links, alternatives) and covers edge cases (failure modes, ecosystem scope).

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 already covers both parameters with descriptions. The description adds context: scoped packages accepted and default version behavior, providing marginal extra value.

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 explicitly states the tool performs a composite check for npm packages across deps.dev and bundlephobia, answering 'should I add this npm package to my project'. It distinguishes from handling other ecosystems directly via deps.dev.

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?

Clearly guides when to use: 'whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also provides alternative for non-NPM packages.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, creating direct ambiguity. The five polymarket tools and the ask/research/entity family (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes) also blur boundaries, making selection error-prone.

Naming Consistency3/5

Most names are snake_case with useful domain prefixes (detroit_*, polymarket_*, ask_pipeworx_*), but verb styles vary widely (ask, generate, scan, validate, suggest, remember) and some names like recent_changes vs recent_alerts are confusable. No strict pattern unifies the set.

Tool Count2/5

At 34 tools the server is overloaded; alongside the core universal-data and Detroit-query tools it also carries memory, subscriptions, feedback, AI-visibility, and npm-scanning tools that feel outside the stated scope. Many of these could be split into separate servers or trimmed.

Completeness3/5

The query surface is broad and the memory/subscription sub-domains have full lifecycle coverage, but the 'Data Detroit' identity is thin (only three city-specific tools) and the catalog-heavy design relies heavily on meta-tools rather than direct data operations. Some gaps remain, like a straightforward way to fetch a raw record by citation.

Resources