How to Use the Classe365 MCP in Pydantic AI

Connect the Classe365 MCP Server to Pydantic AI for type-safe student management. Validate every academic record before your agent acts.

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Connect Classe365 MCP to Pydantic AI

Create your Vinkius account to connect Classe365 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 Classe365 with Pydantic AI

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Type-safe admissions with Pydantic AI

Educational data requires absolute precision. When your agent handles student records, silent failures and hallucinated fields cause massive administrative headaches. This MCP Server forces every response through strict runtime validation. When your agent calls `create_student_profile`, the framework guarantees the payload matches your exact schema before executing the request.

Query attendance without data corruption

Pulling daily participation logs through an MCP often results in messy, unstructured JSON. Your application needs predictable types to function correctly. The agent executes `list_student_attendance` and `list_school_classes` to gather raw logs. If the API returns an unexpected date format, the system fails loudly rather than passing bad data into your application state.

Audit academic assessments reliably

Processing grades demands high accuracy. You can't trust an unconstrained model to summarize test results without strict guardrails. By exposing `list_academic_records` and `list_exam_assessments`, your agent reads department structures and scores directly. Because the framework is model-agnostic, you can swap between local and hosted models to find the most accurate reasoning engine for your specific queries.

Setup guide

Set up Classe365 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": {
        "classe365-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Classe365 tools.",
)

result = await agent.run("List recent Classe365 transactions")
print(result.output)

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Common questions about Classe365 MCP in Pydantic AI

Install the slim package with MCP support. Initialize an MCPToolset with your Vinkius HTTP endpoint and attach it to your agent's toolsets array.

Yes, the integration maps the underlying API directly to your Python environment. Your agent can pull specific profiles and validate the returned fields instantly.

The framework throws a validation error immediately. It blocks the model from proceeding, preventing corrupted information from infecting your downstream processes.

No, Vinkius hosts the infrastructure for you. Your application just connects to the remote endpoint using the unified toolset approach.

Admissions profiles and personal details remain isolated in a single-use container. Vinkius terminates the connection and wipes the memory state the second the request completes.

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