CUFinder MCP Server for Pydantic AIGive Pydantic AI instant access to 13 tools to Bulk Enrich, Check Cufinder Status, Enrich Linkedin, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect CUFinder 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 CUFinder app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 13 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 CUFinder "
"(13 tools)."
),
)
result = await agent.run(
"What tools are available in CUFinder?"
)
print(result.data)
asyncio.run(main())
* 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 CUFinder MCP Server
Connect your CUFinder business intelligence account to any AI agent and simplify how you discover professional domains, enrich company metadata, and identify decision makers through natural conversation.
Pydantic AI validates every CUFinder tool response against typed schemas, catching data inconsistencies at build time. Connect 13 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
- Domain Discovery — Find the primary web domain for any company using only its trade name via AI.
- Company Intelligence — Retrieve detailed metadata including industry, location, and estimated annual revenue for specific domains.
- Employee Prospecting — List known employees and key decision makers associated with a company domain.
- LinkedIn Enrichment — Fetch detailed contact info and professional data from specific LinkedIn profile URLs.
- Lead Qualification — Verify company size and financial standing to prioritize your sales outreach.
- Data Accuracy — Enhance your CRM records with verified real-time data directly from the agent.
The CUFinder MCP Server exposes 13 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 13 CUFinder tools available for Pydantic AI
When Pydantic AI connects to CUFinder through Vinkius, your AI agent gets direct access to every tool listed below — spanning lead-enrichment, company-intelligence, b2b-data, 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
Verify connectivity
Enrich LinkedIn profile
Find company domain
Find email address
Find employees
Find phone number
Get account info
Get company info
Get company revenue
Get social profiles
Get tech stack
Verify email
Connect CUFinder to Pydantic AI via MCP
Follow these steps to wire CUFinder into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the CUFinder MCP Server
Pydantic AI provides unique advantages when paired with CUFinder through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your CUFinder integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your CUFinder connection logic from agent behavior for testable, maintainable code
CUFinder + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the CUFinder MCP Server delivers measurable value.
Type-safe data pipelines: query CUFinder with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple CUFinder tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query CUFinder and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock CUFinder responses and write comprehensive agent tests
Example Prompts for CUFinder in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with CUFinder immediately.
"Find the domain for the company 'Acme Global Solutions'."
"Show me the employees and decision makers for 'apple.com'."
"Enrich the data from this LinkedIn URL: 'https://linkedin.com/in/stevejobs'."
Troubleshooting CUFinder MCP Server with Pydantic AI
Common issues when connecting CUFinder to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiCUFinder + Pydantic AI FAQ
Common questions about integrating CUFinder MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.