How to Use the Lusha MCP in Pydantic AI

Q: Does the Pydantic AI toolset support all Lusha endpoints?

It does. You can access all six tools including findcompany and getcredits within your agent's toolset definition.

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Connect Lusha MCP to Pydantic AI

Create your Vinkius account to connect Lusha 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 Lusha with Pydantic AI

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Perplexity

Type-safe data retrieval

Every response from Lusha is validated against your Pydantic models. If the `find_person` tool returns weird data, your Pydantic AI agent catches it immediately. This prevents bad data from hitting your CRM. You know exactly what you are getting before your code processes it.

Strict lead enrichment

Use `bulk_enrich` to update your prospect records with confidence. The agent enforces your schema, ensuring every phone number and email is properly formatted. You avoid silent failures common with raw API responses. Your agent handles the validation, so you don't have to write custom parsing logic.

Reliable API interaction

Connect via the MCPToolset to ensure your agent stays in sync with the Lusha API. It supports streamable HTTP, making it work well with your existing services. You define the expected structure for `search_contacts` results. If the data doesn't match your model, the agent halts, keeping your system state predictable.

Setup guide

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

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

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

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Common questions about Lusha MCP in Pydantic AI

The framework maps Lusha API outputs to your predefined Pydantic models. If the data structure is invalid, the agent triggers a validation error.

Absolutely. The server provides the tools, and Pydantic AI ensures the data is correct. It creates a stable, predictable foundation for your automation.

It does. You can access all six tools including `find_company` and `get_credits` within your agent's toolset definition.

Your agent only handles data that passes your schema validation. The connection is ephemeral, and you manage the security policies in your local environment.

The agent will raise a validation error. This forces you to handle the discrepancy rather than letting bad data propagate through your systems.

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