How to Use the ENEM Score Calculator MCP in Pydantic AI

Run type-safe ENEM performance analysis and admission simulations with Pydantic AI.

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Connect ENEM Score Calculator MCP to Pydantic AI

Create your Vinkius account to connect ENEM Score Calculator to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ENEM Score Calculator with Pydantic AI

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Type-safe area averages for Pydantic AI

The `calculate_area_averages` tool returns structured data that your Pydantic AI agent validates against defined models. If the math doesn't align with your expected output types, the agent catches it immediately. This keeps your scoring pipeline free from silent errors. You get consistent data structures every time the agent performs a calculation.

Rigorous weighted scoring with Pydantic AI

Use `calculate_weighted_score` to produce a final score that conforms to your strict Python type definitions. The agent ensures that every weight input and score output satisfies your model schema before proceeding. This prevents hallucinated fields or malformed numbers from entering your agent's decision-making process. It guarantees that the data used for final rankings is always valid.

Validate admission simulations in Pydantic AI

The `simulate_admission_possibility` tool provides a clear result that your agent validates to ensure it matches your admission prediction model. Your agent can safely rely on these outputs to inform the end user. This creates a robust link between raw score data and the final simulation result. Your agent remains confident in its output because the data passed through strict validation checks.

Setup guide

Set up ENEM Score Calculator 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": {
        "enem-score-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent ENEM Score Calculator transactions")
print(result.output)

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Common questions about ENEM Score Calculator MCP in Pydantic AI

You initialize an MCPToolset with the URL and pass it to your agent's toolsets list. The framework then handles the execution and validation of every tool result.

Because you are using Pydantic AI, the agent will throw a validation error if the response doesn't match your schema. This prevents bad data from propagating through your system.

Yes, you can configure the MCPToolset to communicate via SSE or Streamable HTTP. This ensures a stable connection for your type-safe agent workflows.

Since Pydantic AI is model-agnostic, you can swap between various LLMs without changing your MCP implementation. The tools will continue to return validated data regardless of the underlying model.

The MCP operates as a stateless calculator that only touches numeric exam scores in transit. It does not cache or store student records, ensuring your data handling remains compliant with your local validation rules.

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