Skip to main content
Glama

Data Santaclara

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

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, etc.), the description adds critical behavioral details: partial failure handling (bundlephobia timeout up to 5-30s, sources_failed list) and return structure. 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?

The description is fairly long but each sentence adds value. It is front-loaded with the core purpose and then details. Slightly verbose but all earned.

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?

Despite no output schema, the description fully details return fields (summary block, per-advisory, links, alternative versions), usage context, limitations, and failure modes. Complete for the complexity.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds meaning beyond the schema: explains scoped packages, version defaults, and implications. Provides context that helps parameter selection.

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 npm packages covering license, advisories, version history, bundle size, etc. It uses specific verbs and resources, and distinguishes from sibling tools.

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 when to use ('whenever an agent asks...') and notes limitations (NPM only v1, fallback for other ecosystems). Provides clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Santaclara toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same 5,798-tool catalog and can return similar evidence-backed answers. Additionally, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, and bet_research all circle prediction-market edge detection, creating boundary ambiguity despite detailed descriptions.

Naming Consistency3/5

Most tools follow a verb_noun or noun_verb pattern (e.g., list_subscriptions, generate_llms_txt, scan_dependency, compare_entities, resolve_entity), and consistent snake_case is used throughout. However, some names are vague and unclear (query, metadata, recall, forget, datasets), and the ask_pipeworx family is not clearly versioned in naming.

Tool Count2/5

34 tools is heavy for a server that is conceptually a data-access gateway plus a few meta utilities. The count is inflated by multiple near-duplicate research modes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and an extensive prediction-market subfamily that could be consolidated.

Completeness4/5

The server covers its main domains well: entity resolution, company profiles, comparisons, change feeds, claim verification, grounded Q&A, and data discovery all exist. Minor gaps include no obvious tool for general web search or full-text legal records, and the subscription/alert system lacks an update-subscription tool, but core workflows have no dead ends.

Resources