Bring Madrid
to CrewAI
Learn how to connect Comunidad de Madrid (Portal Regional) to CrewAI and start using 5 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Comunidad de Madrid (Portal Regional) MCP Server?
Connect your AI agent to the Comunidad de Madrid Open Data Portal to access a wealth of public information directly through natural language. This MCP server provides a bridge to the regional CKAN-based repository, covering everything from transport and health to environment and economy.
What you can do
- Dataset Discovery — Search for specific datasets using keywords like 'transporte', 'salud', or 'medio ambiente' to find relevant public records.
- Metadata Inspection — Retrieve full metadata for datasets, including tags, organizations, and update frequencies.
- Resource Management — List and inspect individual files (resources) within a dataset, such as CSVs, JSONs, or PDFs.
- Direct Data Querying — Use the DataStore integration to query the actual content of datasets directly, allowing for data analysis without manual downloads.
- Portal Exploration — List all available dataset identifiers to understand the scope of available regional data.
How it works
- Subscribe to this server
- (Optional) Enter your Comunidad de Madrid CKAN API Key for higher rate limits
- Start querying regional data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Analysts — quickly pull regional statistics and records into your workflow for analysis.
- Developers — integrate real-time public data from Madrid into applications without navigating complex API docs.
- Researchers & Citizens — find public information about air quality, transport schedules, or economic indicators through simple conversation.
Built-in capabilities (5)
Get full metadata for a specific dataset
Get metadata for a specific resource
List all dataset identifiers in the portal
g., transporte, salud). Search for datasets matching specific criteria
Query data directly from a resource in the DataStore
Why CrewAI?
When paired with CrewAI, Comunidad de Madrid (Portal Regional) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Comunidad de Madrid (Portal Regional) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
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
mcpsparameter 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
Comunidad de Madrid (Portal Regional) in CrewAI
Comunidad de Madrid (Portal Regional) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Comunidad de Madrid (Portal Regional) to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Comunidad de Madrid (Portal Regional) in CrewAI
The Comunidad de Madrid (Portal Regional) 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. All 5 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Comunidad de Madrid (Portal Regional) for CrewAI
Every tool call from CrewAI to the Comunidad de Madrid (Portal Regional) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I find datasets about a specific topic like 'transport'?
Use the search_datasets tool with the query 'transporte'. The agent will return a list of matching datasets with their unique IDs and descriptions from the portal.
Can I see the actual content of a data file without downloading it?
Yes. If the resource is stored in the CKAN DataStore, you can use the search_datastore tool with the Resource ID to query the rows and columns directly.
Is an API key mandatory to use this server?
No, it is optional. However, providing a CKAN_API_KEY allows for higher rate limits and access to restricted datasets if your account has permissions.
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.
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.
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