How to Use the Campinas Open Data MCP in Pydantic AI

Run type-safe queries against Campinas Open Data using Pydantic AI for guaranteed runtime schema validation.

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Connect Campinas Open Data MCP to Pydantic AI

Create your Vinkius account to connect Campinas Open Data 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 Campinas Open Data with Pydantic AI

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Validate Campinas datasets with Pydantic AI

The `get_package` and `get_resource` tools let your Pydantic AI agent fetch Campinas municipal data while enforcing strict Python type validation. If the city's API returns unexpected fields, your agent catches the validation error immediately instead of failing silently. This type-safe approach prevents corrupted data from entering your backend. You get clean, validated Pydantic models representing Brazilian public records, ready for your application logic.

Search city records using this MCP Server

The `search_packages` and `search_resources` tools allow your Pydantic AI agent to run targeted queries against the Campinas database. Mapping raw JSON results directly to your schemas ensures your search results conform to expected types. Because Pydantic AI is model-agnostic, you can use these tools with any LLM provider. Our framework handles the tool calls and validates the output structure before your agent acts on it.

Categorize municipal data by group

The `list_groups` and `list_organizations` tools give your Pydantic AI agent access to the structural categories of the Campinas data portal. Your agent uses these lists to classify public records into validated Python data classes. You initialize this using the MCPToolset class with the Vinkius HTTP endpoint. This MCP integration provides runtime security by checking every city department string against your application's expected formats.

Setup guide

Set up Campinas Open Data 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": {
        "campinas-open-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Campinas Open Data 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 Campinas Open Data Portal. 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 Campinas Open Data MCP in Pydantic AI

Initialize the MCPToolset with your Vinkius HTTP URL and pass it to your Agent constructor. Do not use the deprecated MCPServerHTTP class.

The framework raises a validation error immediately. This keeps your application safe from silent failures when the city updates its metadata schemas.

It supports both Streamable HTTP and SSE transports. Vinkius manages the external MCP Server, so you just point your toolset to the provided HTTP endpoint.

Yes. Pydantic AI is model-agnostic, meaning your local models can call `search_packages` just as easily as commercial APIs.

Yes. Only public, non-identifiable Campinas municipal data is processed. Pydantic AI validates the inputs locally, and Vinkius runs the server in an isolated sandbox to prevent data leaks.

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