How to Use the Mav MCP in Pydantic AI

Q: Can Pydantic AI handle large lead lists from Mav?

Yes. As long as the listleads response matches your Pydantic model, the agent will process the data efficiently and safely.

Use Pydantic AI to enforce strict type-safety for your Mav SMS lead qualification workflows.

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Connect Mav MCP to Pydantic AI

Create your Vinkius account to connect Mav 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 Mav with Pydantic AI

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Validate SMS workflows in Pydantic AI

Your agent uses `create_lead` with schema validation that catches errors before they hit the API. If the data is malformed, Pydantic AI alerts you immediately, preventing bad records from entering your CRM. When your agent calls `get_playbook`, it checks the response structure against your expected model. You never have to worry about missing fields or unexpected data types causing a crash.

Manage lead lifecycle with Pydantic AI

Your agent uses `update_lead` to push changes back to your database with full type checking. It keeps your lead records accurate and reliable, regardless of how complex the SMS conversation gets. If you need to end a sequence, the agent triggers `opt_out_lead`. Because every action is validated, you know the request was handled correctly without any silent failures.

Inspect lead data in Pydantic AI

Your agent calls `list_activities` to pull event logs that are strictly typed. It allows your logic to parse the history without guessing what the API might return. Use `list_leads` to get a clean list of prospects. The agent verifies every lead object against your defined model, ensuring your logic only processes valid, actionable data.

Setup guide

Set up Mav 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": {
        "mav-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Mav MCP in Pydantic AI

Your agent will raise a validation error immediately. This stops the flow before corrupted data can reach your CRM or break your logic.

You pass an MCPToolset pointing to your Vinkius URL into your Agent constructor. The framework handles the connection and validates every tool response automatically.

Yes. As long as the `list_leads` response matches your Pydantic model, the agent will process the data efficiently and safely.

It does. Since it uses standard MCP transport, it integrates perfectly with the latest toolset patterns in the framework.

All lead records are kept in a Vinkius sandbox. Your agent only receives validated, sanitized data that conforms to your Pydantic models, keeping your infrastructure secure.

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