Bring Open Data
to CrewAI
Learn how to connect Ayuntamiento de Barcelona (CKAN) to CrewAI and start using 7 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 Ayuntamiento de Barcelona (CKAN) MCP Server?
Connect to the Ayuntamiento de Barcelona (Open Data BCN) portal and transform public data into actionable insights through natural conversation. This MCP server leverages the CKAN API to provide real-time access to Barcelona's official data repository.
What you can do
- Dataset Discovery — List all available datasets or search for specific topics like transportation, environment, or demographics using
bcn_package_search. - Metadata Inspection — Retrieve detailed metadata for specific datasets and resources to understand data structures and update frequencies.
- Deep Data Querying — Use
bcn_datastore_searchto filter records within CSV resources or execute complexbcn_datastore_search_sqlqueries for precise data extraction. - Resource Management — List all resources associated with a dataset, including download links and format specifications.
How it works
- Subscribe to this server
- Enter your Open Data BCN API Token (optional for public data, required for higher rate limits)
- Start querying Barcelona's public data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Analysts & Researchers — Quickly fetch and filter urban data without manual CSV downloads.
- Urban Planners — Access real-time statistics on city infrastructure and services.
- Developers — Integrate official city data into applications using SQL-like queries directly from the AI.
Built-in capabilities (7)
List datasets including their resources
Search and filter records within a specific CSV resource
The table name must be the resource_id wrapped in double quotes. Execute SQL queries directly against a resource
List all dataset names in the Open Data BCN catalogue
Search for datasets matching specific criteria
Get metadata for a specific dataset
Get metadata for a specific resource
Why CrewAI?
When paired with CrewAI, Ayuntamiento de Barcelona (CKAN) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Ayuntamiento de Barcelona (CKAN) 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
Ayuntamiento de Barcelona (CKAN) in CrewAI
Ayuntamiento de Barcelona (CKAN) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Ayuntamiento de Barcelona (CKAN) 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 Ayuntamiento de Barcelona (CKAN) in CrewAI
The Ayuntamiento de Barcelona (CKAN) 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 7 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
Ayuntamiento de Barcelona (CKAN) for CrewAI
Every tool call from CrewAI to the Ayuntamiento de Barcelona (CKAN) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I perform SQL queries on Barcelona's public datasets?
Yes, you can use the bcn_datastore_search_sql tool to execute SELECT statements directly against any resource that is stored in the portal's datastore.
How do I find datasets related to a specific topic like 'transport'?
Use the bcn_package_search tool with the query parameter q set to your topic. You can also limit the number of results for faster browsing.
Is it possible to see the actual rows of a CSV file without downloading it?
Absolutely. The bcn_datastore_search tool allows you to query and preview the contents of a resource directly, including filtering and sorting options.
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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