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Glama

Data Sncf

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

A4.6/5.0
Behavior5/5

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

Annotations already indicate safe, idempotent, read-only. Description adds composite nature, fan-out behavior, partial failure degradation with sources_failed, and timing caveat (5-30s for new version). No contradictions.

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 and well-structured, front-loaded with purpose. Every sentence adds value, though slightly long; could be marginally shorter without loss of meaning.

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?

For a composite tool with multiple data sources, partial failure modes, and no output schema, the description covers all necessary context: return fields, ecosystem scope, performance caveats, and fallback guidance for other ecosystems.

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% with clear descriptions for both parameters. Description repeats schema info (scoped packages, default version) without adding new semantics beyond what schema provides. Baseline 3 is appropriate.

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?

Clearly states it's a composite check for 'should I add this npm package to my project' using deps.dev and bundlephobia. Unambiguous verb and resource, distinct from all 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 is X safe/popular/small or what does adding lodash cost me.' Also notes NPM ecosystem only and directs to deps.dev:version for other ecosystems, and mentions partial failures.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters are hard to distinguish: ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly', creating a literal duplicate, and the six polymarket_* tools all orbit 'find a trading edge on prediction markets' with only subtle differences in scope. ask_pipeworx / ask_pipeworx_grounded / validate_claim / deep_research also overlap on fact-finding, and discover_tools vs suggest_questions both serve 'what can I do here' discovery. The memory trio and subscription lifecycle are clean, but the central Q&A and prediction-market areas carry real misselection risk.

Naming Consistency2/5

The set mixes several incompatible conventions: bare verbs (query, recall, forget, remember), noun phrases (entity_profile, dataset_info), verb_noun pairs (search_datasets, compare_entities, validate_claim), and prefixed families (polymarket_*, ask_pipeworx_*, pipeworx_*). Family prefixes provide local consistency, but there is no unifying pattern across the server, and the three SNCF tools follow a different style from the Pipeworx tools. The naming reads as several mini-servers bolted together rather than one coherent API.

Tool Count2/5

At 34 tools, the count exceeds the comfortable range and is inflated by genuine redundancy: ask_pipeworx_beta duplicates ask_pipeworx, ask_pipeworx_grounded is a paid variant, scan_competitor_ai_presence wraps ai_visibility_check, and six Polymarket tools could plausibly be consolidated. The server name promises a narrow SNCF data scope, yet 31 of 34 tools serve an unrelated universal data / prediction-market platform, making the count feel both bloated and mismatched to the server's stated identity.

Completeness3/5

For the dominant inferred domain (Pipeworx structured-data Q&A, research, and prediction markets), the surface is quite complete: discovery, routing, grounded answers, deep research, claim verification, entity resolution, profiles, comparisons, change feeds, subscriptions, and memory are all present. However, relative to the server's stated name 'Data Sncf', the SNCF surface is minimal (search -> metadata -> query) with no update feeds, record-level fetch, or live railway status, and the two domains never connect. The orphaned single-purpose tools (generate_llms_txt, scan_dependency) further fragment the sense of a coherent domain.

Resources