Renfe Data MCP Server for Pydantic AIGive Pydantic AI instant access to 11 tools to Ckan Datastore Search, Ckan Package List, Ckan Package Show, and more

Name: Renfe Data
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Renfe Data through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Renfe Data MCP Server for Pydantic AI is a standout in the Iot Hardware category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Renfe Data "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Renfe Data?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Renfe Data MCP Server

Connect to the Renfe Data portal to monitor the Spanish railway network in real-time. This server provides comprehensive access to both live operational data and static historical datasets through the official CKAN infrastructure.

Pydantic AI validates every Renfe Data tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Real-time Tracking — Get precise GPS locations and movement status for Cercanías (commuter) and Long Distance (AVE/LD/MD) trains.
Trip Updates & Delays — Monitor live delays, cancellations, and platform changes to keep travelers informed.
CKAN Portal Access — List, search, and inspect metadata for thousands of railway datasets and resources.
Service Alerts — Retrieve real-time information on accessibility issues, track incidents, or bus substitutions.
Static Schedules — Fetch direct download URLs for GTFS schedules and station lists for offline analysis.

The Renfe Data MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Renfe Data tools available for Pydantic AI

When Pydantic AI connects to Renfe Data through Vinkius, your AI agent gets direct access to every tool listed below — spanning railway, real-time-tracking, gps-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

ckan

Ckan datastore search on Renfe Data

Search for data within a resource

ckan

Ckan package list on Renfe Data

List all dataset names in Renfe Data

ckan

Ckan package show on Renfe Data

Get metadata for a specific dataset

ckan

Ckan resource show on Renfe Data

Get metadata for a specific resource

get

Get avisos on Renfe Data

Get planned service modifications (Avisos)

get

Get static datasets on Renfe Data

List URLs for static datasets (Schedules & Stations)

Rt alerts cercanias on Renfe Data

Updates every 20 seconds. Get real-time service alerts for Cercanías

Rt trip updates cercanias on Renfe Data

Updates every 20 seconds. Get real-time trip updates for Cercanías

Rt trip updates ld on Renfe Data

Updates every 30 seconds. Get real-time trip updates for AV / LD / MD

Rt vehicle positions cercanias on Renfe Data

Updates every 20 seconds. Get real-time vehicle positions for Cercanías

Rt vehicle positions ld on Renfe Data

Updates every 15 minutes. Get real-time vehicle positions for AV / LD / MD

Connect Renfe Data to Pydantic AI via MCP

Follow these steps to wire Renfe Data into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Renfe Data with type-safe schemas

Why Use Pydantic AI with the Renfe Data MCP Server

Pydantic AI provides unique advantages when paired with Renfe Data through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Renfe Data integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Renfe Data connection logic from agent behavior for testable, maintainable code

Renfe Data + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Renfe Data MCP Server delivers measurable value.

Type-safe data pipelines: query Renfe Data with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Renfe Data tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Renfe Data and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Renfe Data responses and write comprehensive agent tests

Example Prompts for Renfe Data in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Renfe Data immediately.

"List all available dataset names in the Renfe Data portal."

"Show me the current real-time positions of Cercanías trains."

"Are there any trip updates or delays for Long Distance trains right now?"

Troubleshooting Renfe Data MCP Server with Pydantic AI

Common issues when connecting Renfe Data to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Renfe Data + Pydantic AI FAQ

Common questions about integrating Renfe Data MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Renfe Data MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.