How to Use the Gobierno Abierto Valencia MCP in Pydantic AI

Q: How does Pydantic AI handle schema changes on the Gobierno Abierto Valencia portal?

When you run showpackage, the framework validates the response against your Pydantic models. If the city updates its API schema, the validation fails instantly, preventing silent data corruption.

Q: How do I handle pagination when querying Gobierno Abierto Valencia with Pydantic AI?

Use the limit and offset parameters in the searchdatastore tool. Your agent can loop through these parameters to fetch data in strict, typed chunks.

Query Valencia's open datasets using this MCP Server with Pydantic AI for strict runtime type safety and zero silent failures.

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Connect Gobierno Abierto Valencia MCP to Pydantic AI

Create your Vinkius account to connect Gobierno Abierto Valencia to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Gobierno Abierto Valencia with Pydantic AI

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Type-safe package lookups with Pydantic AI

The `show_package` tool fetches the metadata and resource list for a specific dataset from the Valencia catalog. Pydantic AI validates this schema at runtime to ensure your agent never processes malformed JSON. That's how you prevent your production code from silently failing or passing corrupt data to downstream models if the portal's API structure changes.

Query structured tables using this MCP Server

The `search_datastore` tool allows your agent to execute targeted searches against Valencia's municipal databases. The returned rows are parsed directly into your defined Pydantic models for immediate use. This ensures that every field, from traffic counts to budget numbers, matches your expected Python types. You get clean, validated data ready for math operations or report generation.

Categorize public records on demand

The `list_groups` tool returns all available high-level categories from the city's open data index. Your agent uses these groups to filter out irrelevant datasets before initiating deep searches. By chaining this with `list_packages`, the agent maps out the city's data structure dynamically. This keeps your runtime light and ensures the model only interacts with relevant municipal files.

Setup guide

Set up Gobierno Abierto Valencia MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "gobierno-abierto-valencia-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Gobierno Abierto Valencia tools.",
)

result = await agent.run("List recent Gobierno Abierto Valencia transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gobierno Abierto Valencia. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Gobierno Abierto Valencia MCP in Pydantic AI

When you run `show_package`, the framework validates the response against your Pydantic models. If the city updates its API schema, the validation fails instantly, preventing silent data corruption.

The server runs as a managed service on Vinkius, so you do not need to host it locally. You connect to the hosted HTTP endpoint using the unified `MCPToolset` class.

Use the limit and offset parameters in the `search_datastore` tool. Your agent can loop through these parameters to fetch data in strict, typed chunks.

The MCP server uses the SSE and Streamable HTTP transports. You can configure your python client to connect to the secure Vinkius endpoint with standard HTTP headers.

All connections run through Vinkius's ephemeral, zero-trust infrastructure. The server only accesses public municipal data, and your query payloads are never written to persistent storage.

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