How to Use the Maranhão Open Data MCP in Pydantic AI

Q: How do I integrate Maranhão Open Data with Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your Vinkius HTTP URL. Pass this toolset directly to your Agent. Do not use the deprecated MCPServerHTTP class.

Q: Does Pydantic AI validate the SQL queries sent to Maranhão Open Data?

Pydantic AI validates the inputs going into searchdatastoresql and the schema of the response coming back. It does not parse the SQL syntax itself, but it ensures the agent's tool call matches the required parameters.

Type-safe access to Maranhão Open Data for Pydantic AI. Enforce strict validation on Brazilian public records before your agent acts.

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Connect Maranhão Open Data MCP to Pydantic AI

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Setup Maranhão Open Data with Pydantic AI

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Perplexity

Type-safe dataset discovery

Government APIs are notorious for returning inconsistent JSON. Pydantic AI fixes this by validating every MCP response. When your agent calls `search_packages`, the framework ensures the returned dataset list matches your expected schema. When the Maranhão portal randomly changes its response format for `list_packages`, your agent fails loudly with a validation error. You get a stack trace immediately. No silent failures. No hallucinated fields making it into your production database.

Query the Maranhão Open Data MCP Server

You want exact answers from public records, not summaries. Your agent uses `search_datastore_sql` to execute raw SQL queries against the portal's datastore. It gets exactly the rows it asks for. For basic lookups, it drops back to `search_datastore`. Because Pydantic AI is model-agnostic, you can swap between Anthropic for writing complex SQL and a cheaper local model for basic key-value searches. The tool execution remains identical.

Validate resource endpoints strictly

Your agent needs to verify what a dataset actually contains before attempting to parse it. It runs `get_package` for the broad metadata, then uses `get_resource` to pinpoint the exact file types. It checks if the resource is a valid CSV or a broken link. By validating this metadata through Pydantic models at runtime, your system guarantees it never passes a malformed URL to your downstream ingestion scripts.

Setup guide

Set up Maranhão 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": {
        "maranhao-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 Maranhão Open Data tools.",
)

result = await agent.run("List recent Maranhão Open Data transactions")
print(result.output)

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Common questions about Maranhão Open Data MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your Vinkius HTTP URL. Pass this toolset directly to your Agent. Do not use the deprecated `MCPServerHTTP` class.

Pydantic AI validates the inputs going into `search_datastore_sql` and the schema of the response coming back. It does not parse the SQL syntax itself, but it ensures the agent's tool call matches the required parameters.

The framework throws a strict validation error. If the Brazilian portal returns a string where an integer was expected, the agent stops execution. It refuses to process corrupted data.

Yes. Since the framework is model-agnostic, you can connect a local Llama model to search packages or list resources, saving API costs on simpler discovery tasks.

You are querying public Brazilian state datasets. The primary security feature here is data integrity. Pydantic AI guarantees that the public records you pull match your schemas, preventing injection of malformed data into your local systems.

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