How to Use the ContactOut MCP in Pydantic AI

Build type-safe prospecting pipelines with Pydantic AI validating every ContactOut email and phone number at runtime.

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Connect ContactOut MCP to Pydantic AI

Create your Vinkius account to connect ContactOut 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 ContactOut with Pydantic AI

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Claude

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Type-safe lead generation with Pydantic AI

Don't let malformed API responses break your production code. This ContactOut MCP Server integrates with Pydantic AI to ensure every payload from `enrich_contact` matches your exact Python schemas before your agent even sees it. If a phone number is missing or an email format is off, the framework catches it instantly. Your agent won't try to send emails to broken addresses because `find_email_by_linkedin` data is validated at the boundary.

Clean company searches without silent failures

When hunting for target accounts, you need reliable data structures. Use `search_companies` and `enrich_company` to pull firmographic details directly into your Pydantic models. If the server returns unexpected fields, Pydantic AI fails loudly so you can handle the exception. This guarantees your database only gets clean, structured company profiles.

Model-agnostic prospecting with strict validation

Whether you use Claude, GPT-4, or local models, this setup keeps your data clean. Use `find_linkedin_by_email` and `search_people` to resolve identities across platforms without worrying about model-specific hallucinations. You can also monitor your billing programmatically by validating the output of `get_api_usage` against your operational budget limits.

Setup guide

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

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

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

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Common questions about ContactOut MCP in Pydantic AI

Install the pydantic-ai-slim package with MCP support. Use the MCPToolset class pointing to your Vinkius HTTP endpoint, and pass it to your Agent to start running `search_people` with full type safety.

Your Pydantic model defines whether the email field is optional. If `find_email_by_linkedin` returns null and your schema requires a string, Pydantic AI will raise a validation error immediately.

Yes, Pydantic AI is model-agnostic. You can connect a local model to the MCP Server to run `enrich_company` searches while keeping your API costs low.

The framework automatically inspects the MCP Server tools, including `enrich_contact`, and maps their input parameters to Pydantic fields, ensuring your agent always sends correctly formatted arguments.

Your queries, professional emails, and phone numbers are processed in-memory within your local runtime. The MCP connection is secured via token authentication, and no prospect data is permanently logged or stored by the gateway.

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