EMT Madrid (Open Data) MCP Server for CrewAIGive CrewAI instant access to 4 tools to Get Bus Arrivals, List Bicimad Stations, Login, and more

Name: EMT Madrid (Open Data)
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

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Connect your CrewAI agents to EMT Madrid (Open Data) through Vinkius, pass the Edge URL in the `mcps` parameter and every EMT Madrid (Open Data) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The EMT Madrid (Open Data) MCP Server for CrewAI is a standout in the Government Public Data category — giving your AI agent 4 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="EMT Madrid (Open Data) Specialist",
    goal="Help users interact with EMT Madrid (Open Data) effectively",
    backstory=(
        "You are an expert at leveraging EMT Madrid (Open Data) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in EMT Madrid (Open Data) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 4 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About EMT Madrid (Open Data) MCP Server

Connect to the EMT Madrid Open Data platform to bring Madrid's mobility network into your AI agent. This server provides direct access to official transport data for the city of Madrid.

When paired with CrewAI, EMT Madrid (Open Data) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call EMT Madrid (Open Data) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Real-time Bus Arrivals — Use get_bus_arrivals to see exactly when the next bus is reaching a specific stop using its unique ID.
BiciMAD Integration — Use list_bicimad_stations to check bike availability and empty docks across the city's electric bike-sharing system.
Route Planning — Use plan_bus_route to find the best way to navigate the city using the EMT bus network from any starting stop.
Official Data — Access the same data used by official apps to ensure accuracy in your mobility workflows.

The EMT Madrid (Open Data) MCP Server exposes 4 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 4 EMT Madrid (Open Data) tools available for CrewAI

When CrewAI connects to EMT Madrid (Open Data) through Vinkius, your AI agent gets direct access to every tool listed below — spanning public-transport, real-time-data, bus-arrivals, 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.

get

Get bus arrivals on EMT Madrid (Open Data)

Get real-time bus arrival times for a stop

list

List bicimad stations on EMT Madrid (Open Data)

Get status of all BiciMAD stations

action

Login on EMT Madrid (Open Data)

plan

Plan bus route on EMT Madrid (Open Data)

Calculate routes between points using the EMT network

Connect EMT Madrid (Open Data) to CrewAI via MCP

Follow these steps to wire EMT Madrid (Open Data) into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 4 tools from EMT Madrid (Open Data)

Why Use CrewAI with the EMT Madrid (Open Data) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with EMT Madrid (Open Data) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

EMT Madrid (Open Data) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the EMT Madrid (Open Data) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries EMT Madrid (Open Data) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries EMT Madrid (Open Data), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain EMT Madrid (Open Data) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries EMT Madrid (Open Data) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for EMT Madrid (Open Data) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with EMT Madrid (Open Data) immediately.

"What are the bus arrival times for stop ID 72?"

"List all BiciMAD stations and their current bike availability."

"Plan a bus route starting from stop 1022."

Troubleshooting EMT Madrid (Open Data) MCP Server with CrewAI

Common issues when connecting EMT Madrid (Open Data) to CrewAI through Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

EMT Madrid (Open Data) + CrewAI FAQ

Common questions about integrating EMT Madrid (Open Data) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

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