Skip to main content
Glama

Data Sncf

by io.github.pipeworx-io

Recent Alerts

recent_alerts
Read-onlyIdempotent

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional — filter to one subscription type.
limitNoMax events to return (1-200, default 50).
sinceNoOptional ISO timestamp — return events fired_at >= this time.
mark_readNoFlag the returned events read in the same call (default false).
unread_onlyNoReturn only events where read_at is null (default false).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior1/5

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

The description discloses that setting mark_read:true flags returned events as read, affecting subsequent calls—a state-modifying behavior. However, the annotations declare readOnlyHint=true, which implies no writes. This contradiction between description and annotation misleads the agent about the tool's side effects, leading to a low score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is four sentences, front-loading the core purpose and then efficiently covering key features (filters, mark_read, polling). Every sentence adds value without redundancy or unnecessary detail.

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?

Despite no output schema, the description explains what fields each alert carries (source, citation_uri, raw payload). It also mentions polling suitability and an alternative endpoint. For a tool with 5 optional parameters, this is sufficiently complete, though it could briefly note the structure of the payload.

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%, providing baseline parameter info. The description adds meaningful context beyond the schema: it gives an example for type ('sec_8k') and explains the effect of mark_read on future calls ('so the next call only shows newer ones'). This extra guidance moderately enhances parameter understanding.

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 it pulls fired events from the subscription feed and returns recent alerts with specific fields (source, citation_uri, raw payload). It distinguishes itself from sibling tools like list_subscriptions, which list subscriptions themselves, not events. The verb 'pull' and resource 'fired events' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (to check recent alerts) and mentions an alternative GET endpoint for scripts/dashboards, but does not explicitly state when not to use it or provide exclusions compared to other tools. The context is clear enough for an agent to infer typical usage.

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

All Data Sncf 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

A3.6/5.0
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