How to Use the Inep Dados Abertos MCP in Pydantic AI

Q: How do I initialize the Inep Dados Abertos MCP Server toolset in Pydantic AI?

You use the MCPToolset class with your Vinkius HTTP endpoint. Pass this toolset to your Agent constructor to automatically register the 12 educational tools.

Q: Can I run complex SQL queries on ENEM data with this setup?

You can use the searchdatastoresql tool to run complex queries. Your agent writes the SQL, and the server returns the requested data from the INEP datastore.

Type-safe Brazilian educational data validation inside your Pydantic AI agent workflows.

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Connect Inep Dados Abertos MCP to Pydantic AI

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

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Type-safe INEP queries using Pydantic AI

The `search_datastore` tool pulls raw rows from the Brazilian school registry, validating the Inep Dados Abertos schema at runtime inside your Pydantic AI agent. Your Pydantic AI agent guarantees that every column in the Inep Dados Abertos response maps to a strict Python type before processing.

Query school records with SQL via MCP Server

The `search_datastore_sql` tool gives your Pydantic AI agent direct access to the Inep Dados Abertos database using raw SQL. This MCP Server ensures that the Inep Dados Abertos query results match your expected Pydantic AI models.

Inspect dataset metadata and tags safely

The `get_package` and `list_tags` tools allow your Pydantic AI agent to verify Inep Dados Abertos metadata before pulling large records. Once verified, the Pydantic AI agent uses `get_resource` to find the exact Inep Dados Abertos endpoint for that year.

Setup guide

Set up Inep 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": {
        "inep-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 Inep Dados Abertos tools.",
)

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

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Common questions about Inep Dados Abertos MCP in Pydantic AI

You use the `MCPToolset` class with your Vinkius HTTP endpoint. Pass this toolset to your Agent constructor to automatically register the 12 educational tools.

Pydantic AI raises a validation error immediately, stopping execution before bad data enters your database. This ensures your pipeline never processes malformed Brazilian school statistics.

Yes, the server supports both Streamable HTTP and SSE transports. Your agent connects securely to the Vinkius endpoint using standard Python async patterns.

You can use the `search_datastore_sql` tool to run complex queries. Your agent writes the SQL, and the server returns the requested data from the INEP datastore.

The server only accesses publicly available open data from INEP, so no private student records are exposed. All transit data is processed inside an ephemeral V8 Isolate MCP Sandbox that destroys the session immediately after execution.

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