How to Use the Dastra MCP in Pydantic AI

Enforce strict type validation on Dastra data in Pydantic AI.

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Connect Dastra MCP to Pydantic AI

Create your Vinkius account to connect Dastra 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 Dastra with Pydantic AI

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Validated DSR handling in Pydantic AI

Every response from `get_dsr` is checked against your Pydantic models before the agent touches it. If the API returns junk, the process halts immediately. Use `create_dsr` to send new requests into the system. The framework ensures your data matches the schema before it ever hits the database.

Type-safe breach management for Pydantic AI

Your agent uses `list_breaches` to pull incident lists into your application state. Pydantic AI validates every field to prevent downstream errors. Create records with `create_breach` while knowing the schema is correct. It removes the risk of sending malformed data to your privacy records.

Strict data mapping with Pydantic AI

Query your processing activities with `list_processings`. The agent receives a clean, typed object that conforms to your local code definitions. Use `list_datasets` and `list_tags` to inventory your data. Your agent maintains consistency across your entire privacy framework.

Setup guide

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

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

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

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Common questions about Dastra MCP in Pydantic AI

Pydantic AI parses the JSON from this MCP server and checks it against your defined Python models. If the data structure deviates, the agent throws a validation error.

Yes, you simply pass the toolset to your agent constructor. Pydantic AI handles the connection and type enforcement automatically.

Vinkius provides a secure HTTP interface for your agent. You authenticate with a single token, keeping your Dastra credentials out of your local code.

Since this is an MCP server, it is model-agnostic. You can pair it with local models or cloud-based LLMs within your Pydantic AI agent.

The server treats DSR records as sensitive privacy data. It limits exposure to only the fields requested by your agent, preventing unnecessary PII leakage.

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