How to Use the Didacte MCP in Pydantic AI

Enforce strict data schemas when querying your Didacte LMS using Pydantic AI's type-safe agent framework.

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Connect Didacte MCP to Pydantic AI

Create your Vinkius account to connect Didacte 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 Didacte with Pydantic AI

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Perplexity

Type-safe course data for Pydantic AI

Every tool call like `get_course_details` returns data validated against your Pydantic models. If the API returns garbage, your agent stops immediately. This prevents silent failures in your training pipelines. You get reliable, typed objects that your application can safely process without worrying about unexpected field types.

Robust learner progress validation

Use `list_active_learning_progress` to track user milestones. Pydantic AI verifies that each progress update matches your expected schema. This keeps your agent logic clean and predictable. You can trust that the data moving through your agent is formatted exactly as your code expects.

Precise organizational user lookups

Get accurate user lists with `list_organization_users`. The MCP Server ensures that every user record is validated before it hits your agent's logic. It eliminates common errors caused by missing or malformed keys in the API response. Your agent spends more time acting on data and less time handling invalid inputs.

Setup guide

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

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

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

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Common questions about Didacte MCP in Pydantic AI

Install the slim MCP package and initialize the MCPToolset with your server URL. Pass this toolset into your agent definition to start making type-safe calls.

Pydantic AI will raise a validation error immediately. This stops the agent from proceeding with bad data, which is exactly how you want your production system to behave.

Yes. It is built to work natively with the async patterns used throughout the Pydantic AI framework, ensuring high performance during concurrent tool calls.

Yes, it is model-agnostic. As long as your agent is built with Pydantic AI, it can talk to this server regardless of which underlying model you choose.

Validation happens locally within your agent's memory. The server only acts as a conduit, and no Didacte learner records are persisted on the infrastructure side.

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