How to Use the ANEEL Dados Abertos MCP in Pydantic AI

Strictly typed ANEEL Dados Abertos integration for Pydantic AI agents.

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Connect ANEEL Dados Abertos MCP to Pydantic AI

Create your Vinkius account to connect ANEEL Dados Abertos 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 ANEEL Dados Abertos with Pydantic AI

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Perplexity

Type-validated ANEEL Dados Abertos queries

Every response from `list_packages` is checked against Pydantic models at runtime. You never deal with malformed data or unexpected API changes. Your Pydantic AI agent fails immediately if the energy data doesn't match your schema. This keeps your state machine predictable and safe.

Reliable resource metadata parsing

The `get_package` tool provides structured metadata that your Pydantic AI models ingest directly. You define the expected structure, and the tool fills it. This eliminates the need for manual parsing code. You trust the data because the framework enforces your type definitions at every step.

Precise record search with Pydantic AI

Use `search_datastore` to pull records that your agent validates against your defined dataclasses. You get exactly the energy fields you asked for. Your Pydantic AI logic remains clean. It works only with the validated objects returned by the ANEEL Dados Abertos server tools.

Setup guide

Set up ANEEL Dados Abertos 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": {
        "aneel-dados-abertos-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent ANEEL Dados Abertos transactions")
print(result.output)

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Common questions about ANEEL Dados Abertos MCP in Pydantic AI

It ensures that the data returned by the server matches your strictly defined Python models. If the server output changes, your agent catches it immediately.

You include the server in your toolsets argument. The framework handles the connection and provides the tools to your agent's tool registry.

The agent raises a validation error. This forces a clean failure rather than allowing your application to process incorrect electricity metrics.

Yes, it supports SSE and streamable HTTP transports. You specify the transport method when you set up the toolset connection.

Vinkius handles the authorization via a single endpoint token. Your agent receives only the filtered, public electricity data you explicitly request.

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