How to Use the Persana AI MCP in Pydantic AI

Q: How do I set up the server for Pydantic AI?

Install the slim package and pass the server URL to MCPToolset. The agent will then discover all 11 tools and apply your validation schemas to each.

Ensure type-safe outbound prospecting with Persana AI and Pydantic AI for reliable agent behavior.

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Connect Persana AI MCP to Pydantic AI

Create your Vinkius account to connect Persana AI 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 Persana AI with Pydantic AI

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Strict type validation with Pydantic AI

Stop worrying about malformed API responses. Every tool like `enrich_company` is wrapped in Pydantic models, so if the data structure is wrong, your agent halts immediately. This prevents silent failures in your prospecting pipeline. You get a clear validation error the moment an API output doesn't match your expected schema.

Reliable lead list management for Pydantic AI agents

Your agent tracks lead lists with confidence using `get_lead_list` and `list_lead_lists`. Pydantic models ensure that list metadata is always parsed correctly. If you need to clean up, `delete_lead_list` runs with the same safety guarantees. You never have to worry about ghost records or corrupted list states.

Intent-driven prospecting with verified data

Get high-fidelity signals using `get_signals` and `find_job_changes` that conform to your defined models. Your agent reads these updates and adjusts its strategy based on validated intent. It uses `verify_email` to guarantee that contact points are real. By strictly validating these inputs, your agent avoids sending outreach to dead ends.

Setup guide

Set up Persana AI 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": {
        "persana-ai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Persana AI MCP in Pydantic AI

You define your Pydantic models in your agent code. The toolset automatically maps the MCP response to these models, ensuring type safety at runtime.

It does. Because every response is validated against a model, any hallucinated fields or unexpected output types will trigger a validation error immediately.

Yes. Since Pydantic AI is model-agnostic, you can swap between OpenAI, Anthropic, or local models while keeping the same toolset definitions.

Install the slim package and pass the server URL to `MCPToolset`. The agent will then discover all 11 tools and apply your validation schemas to each.

Your data is only accessed during the tool execution window. Pydantic AI ensures that no sensitive prospect information is passed outside of your defined, validated logic.

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