How to Use the DISQO MCP in Pydantic AI

Use Pydantic AI with DISQO. Get type-safe consumer insights with runtime validation for every behavioral metric.

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Connect DISQO MCP to Pydantic AI

Create your Vinkius account to connect DISQO 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 DISQO with Pydantic AI

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Claude

Perplexity

Type-safe behavioral metrics for Pydantic AI

Every response from `list_behavioral_metrics` is validated against your Pydantic models. If the API returns unexpected fields, the agent catches the error immediately. This prevents your agent from hallucinating data that doesn't exist. You get reliable, typed structures every time you pull consumer data.

Validated research project lists

Your agent requests active studies using `list_running_research_projects`. The framework enforces strict type checks on the project status and ID fields. This ensures your logic only acts on projects that meet your definition. Invalid data triggers a loud failure rather than silent corruption.

Secure MCP Server communication

Pydantic AI handles the connection to your DISQO server via robust HTTP transports. The toolset integrates directly into your agent definition. It maintains clear separation between your agent logic and the data fetching layer. This keeps your codebase maintainable and strictly typed.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DISQO. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DISQO MCP in Pydantic AI

If the DISQO MCP Server returns data that doesn't match your Pydantic model, the framework raises a validation error. This stops the agent before it can process bad data.

No, you simply initialize the MCPToolset with your server URL. The framework handles the rest, provided your server is reachable.

Yes. Because Pydantic AI is model-agnostic, you can connect your local models to the DISQO server just as easily as cloud-based ones.

Yes, it supports both Streamable HTTP and SSE transports. You can choose the connection method that best fits your deployment environment.

The server uses token-based auth to verify every request. Pydantic AI then enforces strict schema validation, ensuring only authorized, correctly-formatted behavioral data enters your agent's memory.

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