Bring Spain
to Pydantic AI
Learn how to connect CNMC (Comisión Mercados y Competencia) to Pydantic AI and start using 2 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 CNMC (Comisión Mercados y Competencia) MCP Server?
Connect to the official Open Data portal of the CNMC (Comisión Nacional de los Mercados y la Competencia). This server allows AI agents to explore and extract public data regarding telecommunications, energy, transport, and postal sectors in Spain.
What you can do
- Catalog Discovery — Use
package_searchto find datasets, resources, and metadata using keywords or specific queries. - Data Extraction — Use
datastore_searchto pull actual records and rows from specific resources using their unique IDs. - Granular Filtering — Apply JSON filters to narrow down results to specific dates, regions, or categories within a dataset.
- Resource Inspection — Identify update frequencies, license types, and resource formats before processing data.
How it works
- Subscribe to this server
- Enter your CNMC API Key (if required for higher rate limits)
- Start querying Spanish market data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Analysts — quickly fetch market statistics without manual CSV downloads
- Legal & Compliance Teams — monitor regulatory data and competition records
- Policy Researchers — access public sector information for economic modeling and reporting
Built-in capabilities (2)
g., a CSV file within a dataset). Requires a resource_id obtained via package_search. Retrieve actual records from a specific CNMC resource
Use this to find the resource_id needed for datastore_search. Search for datasets and resources in the CNMC catalog
Why Pydantic AI?
Pydantic AI validates every CNMC (Comisión Mercados y Competencia) tool response against typed schemas, catching data inconsistencies at build time. Connect 2 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.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your CNMC (Comisión Mercados y Competencia) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your CNMC (Comisión Mercados y Competencia) connection logic from agent behavior for testable, maintainable code
CNMC (Comisión Mercados y Competencia) in Pydantic AI
CNMC (Comisión Mercados y Competencia) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect CNMC (Comisión Mercados y Competencia) to Pydantic AI 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 CNMC (Comisión Mercados y Competencia) in Pydantic AI
The CNMC (Comisión Mercados y Competencia) 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 2 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI 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
CNMC (Comisión Mercados y Competencia) for Pydantic AI
Every tool call from Pydantic AI to the CNMC (Comisión Mercados y Competencia) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I find the resource_id needed for data retrieval?
Use the package_search tool with a query related to your topic. The response will include a list of resources, each with a unique id (the resource_id) that you can then use in datastore_search.
Can I limit the number of records returned to avoid large payloads?
Yes! The datastore_search tool includes an optional limit parameter. You can specify exactly how many rows you want to retrieve (e.g., 10 or 100) to keep the response concise.
Is it possible to filter data by a specific field, like a year or a city?
Absolutely. Use the filters parameter in datastore_search. It accepts a JSON string (e.g., {"Year": "2023"}) to return only the records that match your criteria.
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 CNMC (Comisión Mercados y Competencia) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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