Where do I find my kvCORE API Key?

Log in to your kvCORE dashboard, navigate to the **Marketplace** or **API** settings section to generate a token.

Can I update lead status with this server?

Yes, use the `update_lead_info` tool and provide the fields you want to modify in a JSON string.

Does this support MLS searches?

The `list_property_listings` tool retrieves properties available within your kvCORE integrated MLS data.

kvCORE MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: kvCORE
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

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 kvCORE "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 kvCORE MCP Server

Connect your AI agent to kvCORE, the primary platform for real estate professionals to manage their entire business.

Pydantic AI validates every kvCORE tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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.

Key Features

Lead Management — Search and audit lead profiles, update statuses, and add manual notes through natural language
Listing Intelligence — Access active property listings and detailed metadata including features and pricing
Marketing Visibility — List and monitor smart campaigns and automated marketing sequences
Agent Workflow — Audit pending tasks, reminders, and recent lead activities to stay on top of follow-ups
Profile Insights — Fetch agent profile data and high-level account configuration

Simple Setup

1. Subscribe to this server
2. Log in to kvCORE, go to Settings > API, and generate an API Key
3. Enter your key in the configuration panel
4. Start managing your real estate business via chat

The kvCORE MCP Server exposes 10 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 kvCORE to Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

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

Explore tools

The agent discovers 10 tools from kvCORE with type-safe schemas

Why Use Pydantic AI with the kvCORE MCP Server

Pydantic AI provides unique advantages when paired with kvCORE 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 kvCORE integration code

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

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

kvCORE + Pydantic AI Use Cases

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

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

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

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

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

kvCORE MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect kvCORE to Pydantic AI via MCP:

create_lead_note

Add a note to a lead profile

get_agent_profile

Get current agent information

get_lead_details

Get details for a specific lead

get_listing_details

Get metadata for a specific listing

list_agent_tasks

List pending tasks for the agent

list_lead_activity

List recent activity for a lead

list_marketing_campaigns

List all marketing campaigns

list_property_listings

List active property listings

search_kvcore_leads

Returns lead IDs and basic contact info. Search for leads in kvCORE

update_lead_info

g., status, phone). Provide data as a JSON string. Update an existing lead

Example Prompts for kvCORE in Pydantic AI

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

"Search for a lead named 'Alice' in my kvCORE"

"Show me details for the property at '123 Maple St'"

"List my tasks for today"

Troubleshooting kvCORE MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

kvCORE + Pydantic AI FAQ

Common questions about integrating kvCORE MCP Server with Pydantic AI.

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.

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.

Can I switch LLM providers without changing MCP code?

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