How to Use the DigitalChalk MCP in Pydantic AI

Q: How do I install the DigitalChalk support for Pydantic AI?

Use pip install "pydantic-ai-slimmcp" to get the necessary dependencies. This setup provides the unified toolset needed to connect your agent to your LMS.

Type-safe DigitalChalk tools for Pydantic AI. Ensure data integrity in your agent workflows with runtime validation.

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Connect DigitalChalk MCP to Pydantic AI

Create your Vinkius account to connect DigitalChalk 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 DigitalChalk with Pydantic AI

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Perplexity

Strictly typed DigitalChalk responses

Every response from `get_user_learning_profile` is validated against Pydantic models. If the API returns malformed data, your agent stops immediately. This prevents silent errors from propagating through your system. You get reliable, clean data every time your agent interacts with the LMS.

Validated exam and user toolsets

Use `list_assessment_exams` and `list_lms_users` to gather data with confidence. The framework ensures that fields like completion dates or grade percentages match your expected types. It removes the need for manual parsing code. Your agent interacts with the API through a robust, type-safe interface that catches issues at the boundary.

Reliable enrollment monitoring

Track user enrollments using `list_user_enrollments` while maintaining strict schema compliance. Your agent handles course data without guessing about the response structure. This keeps your production logic stable. You define the model, and the server ensures the incoming data conforms to your requirements.

Setup guide

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

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

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

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Common questions about DigitalChalk MCP in Pydantic AI

If the API returns unexpected data, Pydantic AI throws a validation error. This prevents your agent from processing corrupted information or hallucinating values.

It is. The server supports the required transports for Pydantic AI. You initialize the `MCPToolset` with your server URL to start using the tools.

You can. Because the framework is model-agnostic, you can pair the DigitalChalk tools with any model you choose. The validation happens at the tool level, regardless of the LLM.

Use `pip install "pydantic-ai-slim[mcp]"` to get the necessary dependencies. This setup provides the unified toolset needed to connect your agent to your LMS.

Your data is validated at runtime and never persisted in a way that risks exposure. The connection between your agent and the DigitalChalk API uses secure tokens to ensure only authorized requests access your records.

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