2,500+ MCP servers ready to use
Vinkius

Lusha MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lusha through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Vinkius supports streamable HTTP and SSE.

python
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Lusha "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Lusha?"
    )
    print(result.data)

asyncio.run(main())
Lusha
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lusha MCP Server

Connect Lusha to your AI agent and access verified B2B contact data for your sales prospecting.

Pydantic AI validates every Lusha tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

  • Contact Enrichment — Find verified emails, direct dial phone numbers, and social profiles for any prospect.
  • Company Data — Pull firmographic data including industry, employee count, revenue range, and headquarters location.
  • Prospect Search — Build targeted prospect lists filtered by job title, seniority, company size, and geography.
  • Bulk Enrichment — Enrich multiple contacts at once from your existing CRM or lead lists.

The Lusha MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Lusha to Pydantic AI via MCP

Follow these steps to integrate the Lusha MCP Server with Pydantic AI.

01

Install Pydantic AI

Run pip install pydantic-ai

02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

03

Run the agent

Save to agent.py and run: python agent.py

04

Explore tools

The agent discovers 6 tools from Lusha with type-safe schemas

Why Use Pydantic AI with the Lusha MCP Server

Pydantic AI provides unique advantages when paired with Lusha through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lusha integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your Lusha connection logic from agent behavior for testable, maintainable code

Lusha + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Lusha MCP Server delivers measurable value.

01

Type-safe data pipelines: query Lusha with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple Lusha tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query Lusha and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock Lusha responses and write comprehensive agent tests

Lusha MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Lusha to Pydantic AI via MCP:

01

bulk_enrich

Bulk enrich emails

02

find_by_linkedin

Find by LinkedIn URL

03

find_company

Find company

04

find_person

Find person

05

get_credits

Get credit balance

06

search_contacts

Search contacts

Example Prompts for Lusha in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Lusha immediately.

01

"Find the contact details for John Smith, VP Sales at TechCo."

02

"Enrich this list of 5 contacts from my CRM."

03

"Search for marketing directors at fintech companies in London."

Troubleshooting Lusha MCP Server with Pydantic AI

Common issues when connecting Lusha to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Lusha + Pydantic AI FAQ

Common questions about integrating Lusha MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Lusha MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

Connect Lusha to Pydantic AI

Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.