How to Use the Cuiabá Transparency MCP in Pydantic AI

Q: How do I hook up Cuiabá Transparency to Pydantic AI?

Run pip install pydantic-ai-slimmcp. Create an MCPToolset pointing to your Vinkius URL. Pass that object into the toolsets list when defining your Agent.

Force strict type validation on Cuiabá municipal records using Pydantic AI and this MCP Server.

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Connect Cuiabá Transparency MCP to Pydantic AI

Create your Vinkius account to connect Cuiabá Transparency 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 Cuiabá Transparency with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Validate city budgets with Pydantic AI

Government APIs change without warning. Running `list_budget` or `list_revenues` through this framework guarantees you get the exact schema you expect. If the city adds a weird string to a currency field, your agent fails loudly. Silent corruption destroys data pipelines. By enforcing strict Pydantic models on the MCP response, you ensure your downstream analytics only process clean, verified numbers.

Parse public tenders safely

Extracting vendor names from municipal contracts triggers hallucinations in most agents. Calling `list_contracts` pulls the raw text directly. The framework then forces the agent to map that text into your defined Python classes. You pick the model. Routing the prompt to Claude, OpenAI, or a local Llama instance keeps the extraction logic identical. The framework forces the LLM to output valid types regardless of the backend.

Audit payroll through an MCP Server

Investigating public servant records requires absolute accuracy. Triggering `list_personnel` and `list_expenses` pulls the raw salary and spending data. Your typesafe agent processes these records without guessing field names. Integration relies on the unified toolset approach. You instantiate `MCPToolset` with your Vinkius HTTP URL and drop it into the toolsets array. The deprecated MCPServerHTTP class is gone.

Setup guide

Set up Cuiabá Transparency 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": {
        "cuiaba-transparency-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Cuiabá Transparency 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 Cuiabá Transparency 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 Cuiabá Transparency MCP in Pydantic AI

Run pip install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to your Vinkius URL. Pass that object into the toolsets list when defining your Agent.

The framework handles it natively. It intercepts the raw JSON returned by tools like list_budget and checks the payloads against your custom schemas before the agent acts on them.

Yes. The framework is completely model-agnostic. You can fetch public personnel records from the server and have a local Llama instance parse the results.

Vinkius uses HTTP. You must pass your endpoint URL to MCPToolset. The framework handles the Streamable HTTP connection under the hood.

We handle the server side with strict isolation. When you query public servant names or department spending, the Vinkius container executes the tool and vanishes. No logs remain on our infrastructure.

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