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Lusha MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Bulk Enrich Companies, Bulk Enrich Persons, Enrich Company Info, and more

Built by Vinkius GDPR 12 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.

Ask AI about this App Connector for Pydantic AI

The Lusha app connector for Pydantic AI is a standout in the Industry Titans category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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 "
            "(12 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 your Lusha account to any AI agent and take full control of your sales prospecting and data enrichment through natural conversation. Lusha provides a premier B2B database, and this integration allows you to retrieve high-fidelity contact details (email, phone), enrich company metadata, and search for new prospects directly from your chat interface.

Pydantic AI validates every Lusha tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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 & Person Enrichment — Lookup detailed contact metadata programmatically using name, company, or LinkedIn URLs to ensure your CRM is always synchronized.
  • Company & Firmographic Intelligence — Access and monitor company data including industry, revenue, and headcount directly from the AI interface to qualify accounts in real-time.
  • Prospecting & Search Control — Search for new contacts and companies matching your Ideal Customer Profile (ICP) via natural language to drive better sales efficiency.
  • Usage & Credit Oversight — Access granular details for your credit consumption and remaining balance using simple AI commands to maintain a clear overview of your resources.
  • Operational Monitoring — Track system responses and manage data ingestion to ensure your sales workflows are always optimized.

The Lusha MCP Server exposes 12 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.

All 12 Lusha tools available for Pydantic AI

When Pydantic AI connects to Lusha through Vinkius, your AI agent gets direct access to every tool listed below — spanning b2b-intelligence, data-enrichment, prospecting, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

bulk_enrich_companies

Enrich multiple companies

bulk_enrich_persons

Enrich multiple contacts

enrich_company_info

Get firmographics

enrich_person_info

Get contact details

get_account_info

Check connection

get_credit_balance

Check account balance

get_person_by_email

Enrich by email

get_person_by_linkedin

Enrich by LinkedIn

get_usage_stats

Check API usage

prospect_new_companies

Search for businesses

prospect_new_leads

Search for contacts

test_lusha_auth

Verify API key

Connect Lusha to Pydantic AI via MCP

Follow these steps to wire Lusha into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

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 12 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

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

"Enrich this contact: John Miller at Acme Corp."

02

"Search for companies in New York with 500-1000 employees in the SaaS industry."

03

"Check my Lusha credit balance."

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.