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Dati It

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context: partial failures, bundlephobia timeout (5-30s), graceful degradation with sources_failed list, and return structure details.

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?

The description is a single dense paragraph but efficiently conveys purpose, usage, limitations, and output structure. Every sentence serves a purpose, though it could be slightly more structured with bullet points for the return fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but the description details all return fields. Parameter schema is fully covered. The tool complexity (composite, external dependencies) is well addressed with timeouts and failure modes. Additional context like ecosystem scope and partial failure handling makes it complete.

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. The description adds meaning by explaining default version behavior (latest when omitted), acceptance of scoped packages, and outlines the output fields (summary block, advisory detail, links, alternative versions), which is not in the schema.

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 specifies the tool is a composite 'should I add this npm package' check, covering licenses, advisories, version history, bundle size, and tree-shaking support. It distinguishes from siblings by focusing on npm ecosystem and single-call evaluation.

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?

The description explicitly states when to use this tool: when an agent asks about safety, popularity, size, or cost of adding an npm package. It also directs agents to deps.dev for non-npm ecosystems, providing clear 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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same 5,529 tools; polymarket_arbitrage and polymarket_edges both find tradeable opportunities; discover_tools and suggest_questions both serve discovery. The beta tool being an exact duplicate makes misselection highly likely.

Naming Consistency3/5

Most action tools follow verb_noun (ask_pipeworx, compare_entities, discover_tools, list_groups, resolve_entity, search_datasets, suggest_questions, validate_claim), but there is significant mixing with noun_noun (dataset_details, entity_profile, organization_details, pipeworx_feedback, polymarket_arbitrage) and adjective_noun (deep_research, recent_alerts). The Polymarket family is consistently prefixed, but overall the server mixes several conventions.

Tool Count2/5

36 tools far exceeds the typically well-scoped range, and the server bundles what appear to be five separate concerns: Italian open data, Pipeworx universal query, entity/report utilities, prediction-market analytics, and meta/memory/subscription features. Many tools could be consolidated (e.g., ai_visibility_check and scan_competitor_ai_presence; discover_tools and suggest_questions), making the set feel bloated.

Completeness4/5

The broad domain of structured data research and prediction-market edge is largely covered: universal routing, grounded answers, deep research, entity resolution, profiles, comparisons, change feeds, claim verification, arbitrage scans, fill-risk, subscriptions, memory, and feedback. Minor gaps exist—no direct tool to fetch raw CKAN resource URLs, no exhaustive list of all 5,529 tools, and no actual order execution on prediction markets—but these are workable around.

Resources