Which integrations are available for this server?

Used for version control and distribution of the MCP server code, with instructions for cloning the repository. Hosts the repository for the MCP server, with instructions for cloning from GitHub provided in the installation section. Provides comprehensive testing capabilities for the MCP server, including unit tests, integration tests, and coverage reporting. The server is built with Python, with examples showing how to install dependencies and run the server using Python commands.

How do I use Real Estate MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Real Estate MCP Server find 3-bedroom homes in Seattle under $800k" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Real Estate MCP Server

A comprehensive Model Context Protocol (MCP) server for real estate data management. This server provides tools, resources, and prompts for property listings, agent management, market analysis, client relationships, and area intelligence.

🏗️ Architecture

The server is built with a modular, componentized architecture for maintainability and scalability:

real-estate-mcp/ ├── main.py # Main server entry point ├── utils.py # Core data management utilities ├── tools/ # MCP Tools (organized by category) │ ├── property_tools.py # Property search, filtering, insights │ ├── agent_tools.py # Agent profiles, performance, dashboards │ ├── market_tools.py # Market analysis and trends │ ├── client_tools.py # Client management and matching │ ├── area_tools.py # Area intelligence and amenities │ └── system_tools.py # Data management and system tools ├── resources/ # MCP Resources (organized by domain) │ ├── property_resources.py # Property-related resources │ ├── agent_resources.py # Agent-related resources │ ├── market_resources.py # Market analysis resources │ ├── client_resources.py # Client management resources │ └── location_resources.py # Area and amenity resources ├── prompts/ # MCP Prompts (user-controlled templates) │ ├── __init__.py # Central prompt registration │ ├── property_prompts.py # Property analysis and comparison prompts │ ├── client_prompts.py # Client matching and consultation prompts │ ├── market_prompts.py # Market analysis and investment prompts │ └── agent_prompts.py # Agent performance and development prompts └── data/ # Real estate data files ├── properties/ ├── agents/ ├── clients/ ├── market/ ├── transactions/ ├── areas/ └── amenities/

Related MCP server: Zillow MCP Server

🚀 Features

MCP Capabilities

30+ Tools: Comprehensive real estate operations
10 Resources: 5 static resources + 5 dynamic resource templates
11 Prompts: User-controlled analysis templates across 4 categories
SSE Transport: Web-compatible Server-Sent Events endpoint

Tool Categories

🏠 Property Management (7 tools)

Search and filter properties by multiple criteria
Get property details and comprehensive insights
Area-based and agent-based property listings
Market context and comparable analysis

👥 Agent Operations (6 tools)

Agent profiles and specializations
Performance dashboards and metrics
Client and property portfolio management
Sales tracking and analytics

📊 Market Analysis (7 tools)

Market overview and price analytics
Area-specific market performance
Investment opportunity analysis
Comparative area analysis
Transaction tracking

🤝 Client Management (3 tools)

Client profiles and preferences
Property matching algorithms
Budget and criteria-based recommendations

🏘️ Area Intelligence (9 tools)

Comprehensive area reports
Amenities and demographics
Schools, parks, shopping, healthcare data
City overview and area comparisons

⚙️ System Management (2 tools)

Data refresh and cache management
System statistics and summaries

Resources

Static Resources

realestate://all-properties: Complete property listings
realestate://all-agents: Agent directory
realestate://market-overview: Current market trends
realestate://all-areas: Area information
realestate://amenities: Complete amenities database

Dynamic Resource Templates

realestate://properties/area/{area}: Area-specific properties
realestate://agent/{agent_id}/dashboard: Agent performance dashboard
realestate://market/area/{area}: Area market analysis
realestate://property/{property_id}/insights: Property insights
realestate://client/{client_id}/matches: Client property matches

Prompts (11 total)

Property Prompts (2 prompts)

Property Analysis: Comprehensive property evaluation and insights
Property Comparison: Side-by-side property comparison analysis

Client Prompts (3 prompts)

Client Matching: Personalized property recommendations
Client Consultation: Structured consultation framework
Client Feedback Analysis: Search strategy refinement

Market Prompts (3 prompts)

Market Reports: Comprehensive area market analysis
Investment Analysis: ROI and opportunity assessment
Comparative Market Analysis: Multi-area comparison

Agent Prompts (3 prompts)

Agent Performance: Performance dashboards and analysis
Agent Marketing Strategy: Business development and marketing
Agent Training Development: Skill enhancement and training plans

📦 Installation

Quick Start (Recommended)

# Clone the repository git clone https://github.com/agentic-ops/real-estate-mcp.git cd real-estate-mcp # Create and activate virtual environment python -m venv .venv # On Windows: .venv\Scripts\activate # On macOS/Linux: source .venv/bin/activate # Install dependencies pip install -r requirements.txt # Install in Claude Desktop mcp install main.py

Manual Claude Desktop Configuration

If you prefer manual setup, edit your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{ "mcpServers": { "real-estate": { "command": "C:/absolute/path/to/real-estate-mcp/.venv/Scripts/python.exe", "args": ["C:/absolute/path/to/real-estate-mcp/main.py"], "env": { "PYTHONUNBUFFERED": "1" } } } }

Important:

Use absolute paths for both the Python executable and main.py
On Windows, use forward slashes (/) or double backslashes (\\) in the JSON
Point to your virtual environment's Python (.venv/Scripts/python.exe on Windows, .venv/bin/python on macOS/Linux)
Restart Claude Desktop completely after configuration

Running Locally (For Testing)

# STDIO mode (default, for Claude Desktop) python main.py # SSE mode (for remote/web access) python main.py sse

🔍 MCP Inspector

To inspect and debug your MCP server during development:

npx @modelcontextprotocol/inspector python main.py

This will launch the MCP Inspector interface, allowing you to:

Monitor MCP messages in real-time
Debug tool and resource calls
Inspect server responses
Test server functionality

💻 Claude Desktop Integration

Quick Setup

Once your server is installed in Claude Desktop (see Installation above), you can immediately start using it:

Restart Claude Desktop after configuring the server
Look for the MCP indicator (🔨 hammer icon) in the bottom-right corner
Click the indicator to see available tools and resources
Start chatting - Claude can now access all your real estate data!

Example Prompts for Claude

Try these prompts to explore the real estate data:

"What properties are available in Downtown Riverside?"
"Show me properties between $500k and $1M"
"Give me agent Sarah Chen's performance dashboard"
"What's the current market overview?"
"Match properties for client CLI001"
"Tell me about schools in the Woodcrest area"
"Compare market conditions across all areas"

Configuration Details

Windows: Edit %APPDATA%\Claude\claude_desktop_config.json macOS: Edit ~/Library/Application Support/Claude/claude_desktop_config.json

{ "mcpServers": { "real-estate": { "command": "python", "args": ["C:/absolute/path/to/real-estate-mcp/main.py"], "env": { "PYTHONUNBUFFERED": "1" } } } }

Important Notes:

Use absolute paths to your main.py file
Adjust the Python command if you're using a virtual environment
The server runs in STDIO mode by default (no port or URL needed)

🌐 Server Transport Modes

This server supports two transport modes for different use cases:

STDIO Transport (Default - Recommended for Claude Desktop)

Best for: Claude Desktop, MCP Inspector, local development

Advantages:

✅ Ultra-low latency (~1ms)
✅ Simple configuration (no ports/URLs)
✅ Automatic process management
✅ Standard MCP ecosystem compatibility

Usage:

python main.py

SSE Transport (For Remote/Web Access)

Best for: Remote access, web integration, team sharing, cloud deployment

Advantages:

✅ Multiple simultaneous clients
✅ Remote access over network
✅ Web browser compatible
✅ Real-time streaming updates

Usage:

python main.py sse

SSE Connection Details (When Running in SSE Mode)

SSE Endpoint: http://127.0.0.1:8000/sse (for establishing SSE connection)
Message Endpoint: http://127.0.0.1:8000/messages/ (for posting MCP messages)
Transport: SSE (Server-Sent Events)
Protocol: MCP (Model Context Protocol)

Web Client Example

// Establish SSE connection const eventSource = new EventSource('http://127.0.0.1:8000/sse'); eventSource.onmessage = function(event) { const mcpMessage = JSON.parse(event.data); // Handle MCP protocol messages }; // Send MCP messages async function sendMCPMessage(message) { const response = await fetch('http://127.0.0.1:8000/messages/', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify(message) }); return response.json(); }

🔧 Component Details

Core Components

`utils.py` - Data Management

RealEstateDataManager: Central data access class
PropertyFilter: Search and filtering utilities
JSON data loading and caching
Cross-referencing and relationship mapping

`main.py` - Server Entry Point

FastMCP server initialization
Component registration orchestration
SSE transport configuration
Startup logging and diagnostics

Tool Modules

Each tool module follows a consistent pattern:

def register_[category]_tools(mcp: FastMCP): """Register all [category] tools with the MCP server""" @mcp.tool() def tool_function(parameters) -> str: """Tool description""" # Implementation return json.dumps(result, indent=2)

Resource Modules

Resources are organized by domain for better maintainability:

Property Resources (`property_resources.py`)

Property listings and search results
Property insights and market context
Area-based property filtering

Agent Resources (`agent_resources.py`)

Agent profiles and directories
Performance dashboards and metrics

Market Resources (`market_resources.py`)

Market overview and trends
Area-specific market analysis

Client Resources (`client_resources.py`)

Client preference matching
Property recommendations

Location Resources (`location_resources.py`)

Area information and demographics
Amenities and local services

Each module follows a consistent pattern:

def register_[domain]_resources(mcp: FastMCP): """Register all [domain] resources with the MCP server""" @mcp.resource("realestate://resource-name") def resource_function() -> str: """Resource description""" return json.dumps(data, indent=2)

Prompt Templates

Prompts guide AI analysis:

@mcp.prompt() def analysis_prompt(param: str = "default") -> str: """Analysis prompt description""" return f""" Detailed analysis instructions for {param}... """

📊 Data Structure

The server operates on comprehensive real estate data:

5 Properties: Victorian homes, contemporary, luxury, townhouses
3 Agents: Specialized real estate professionals
6 Clients: Buyers, sellers, investors with preferences
Multiple Sales: Recent transaction history
5 Areas: Downtown Riverside, Woodcrest, Canyon Crest, Arlington Heights, La Sierra
Amenities: Schools, parks, shopping, healthcare facilities

🔍 Usage Examples

Using with Claude Desktop (STDIO Mode - Recommended)

After installing in Claude Desktop, simply chat with Claude:

User: "What properties are available in Downtown Riverside?"

Claude: Uses the

User: "Show me Sarah Chen's performance dashboard"

Claude: Reads the

User: "Find properties under $800k with 3+ bedrooms"

Claude: Uses the

Using with MCP Inspector (For Testing)

# Test with MCP Inspector npx @modelcontextprotocol/inspector python main.py

Then explore tools and resources through the inspector UI.

Using with SSE Transport (For Remote/Web Access)

For proper MCP client integration over HTTP, use the MCP protocol with the correct endpoints:

# Start server in SSE mode python main.py sse

# Establish SSE connection (listen for server messages) curl -N http://127.0.0.1:8000/sse # Send MCP messages (in a separate terminal) # Search properties curl -X POST http://127.0.0.1:8000/messages/ \ -H "Content-Type: application/json" \ -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "search_properties", "arguments": {"query": "Victorian"}}}' # Filter by criteria curl -X POST http://127.0.0.1:8000/messages/ \ -H "Content-Type: application/json" \ -d '{"jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": {"name": "filter_properties", "arguments": {"min_price": 500000, "max_price": 1000000}}}' # Get market overview curl -X POST http://127.0.0.1:8000/messages/ \ -H "Content-Type: application/json" \ -d '{"jsonrpc": "2.0", "id": 3, "method": "resources/read", "params": {"uri": "realestate://market-overview"}}' # Match client preferences curl -X POST http://127.0.0.1:8000/messages/ \ -H "Content-Type: application/json" \ -d '{"jsonrpc": "2.0", "id": 4, "method": "tools/call", "params": {"name": "match_client_preferences", "arguments": {"client_id": "CLI001"}}}'

Using SSE Mode with Claude Desktop

If you want to run the server in SSE mode and connect from Claude Desktop (for remote scenarios):

Start server: python main.py sse
Configure Claude Desktop with mcp-remote:

{ "mcpServers": { "real-estate-remote": { "command": "npx", "args": ["-y", "mcp-remote", "http://127.0.0.1:8000/sse"] } } }

🧪 Testing

The project includes a comprehensive test suite covering all components and functionality.

Test Structure

tests/ ├── conftest.py # Pytest configuration and shared fixtures ├── unit/ # Unit tests for core components │ ├── test_utils.py # RealEstateDataManager and PropertyFilter tests │ └── test_*.py # Additional unit tests ├── integration/ # Integration tests for MCP components │ ├── test_property_tools.py # Property tools integration tests │ ├── test_all_tools.py # All other tool categories │ ├── test_resources.py # Static and template resources tests │ └── test_prompts.py # Prompt template tests └── __init__.py

Test Categories

Unit Tests (`tests/unit/`)

Data Manager Tests: Core functionality of RealEstateDataManager
Filter Tests: Property filtering logic and edge cases
Utility Functions: Helper functions and data validation

Integration Tests (`tests/integration/`)

Property Tools: Search, filter, insights, and area-based queries
Agent Tools: Profile management, performance dashboards
Market Tools: Market analysis and trend calculations
Client Tools: Client matching and preference algorithms
Area Tools: Area intelligence and amenities data
System Tools: Data refresh and system statistics
Resources: Static resources and dynamic templates
Prompts: Template generation and parameter handling (11 prompts across 4 categories)

Running Tests

Prerequisites

# Install testing dependencies pip install -r requirements.txt

Quick Test Commands

# Run all tests pytest # Run with coverage report pytest --cov=. --cov-report=html # Run specific test categories pytest tests/unit/ # Unit tests only pytest tests/integration/ # Integration tests only pytest tests/integration/test_property_tools.py # Property tools only

Using the Test Runner Script

# Run all tests python run_tests.py # Run specific test types python run_tests.py unit # Unit tests only python run_tests.py integration # Integration tests only python run_tests.py property # Property tools only python run_tests.py resources # Resource tests only # Run with verbose output and coverage python run_tests.py all -v -c

Test Features

Fixtures and Test Data

Isolated Test Environment: Each test uses temporary data directories
Mock Data: Consistent test data across all test cases
Shared Fixtures: Reusable test components in conftest.py
Data Manager Mocking: Isolated testing without file system dependencies

Coverage and Reporting

Code Coverage: Comprehensive coverage reporting with pytest-cov
HTML Reports: Visual coverage reports in htmlcov/index.html
Missing Lines: Identification of uncovered code paths
Branch Coverage: Logic branch testing

Test Configuration

pytest.ini: Centralized test configuration
Automatic Discovery: Tests auto-discovered by naming convention
Parallel Execution: Support for parallel test execution
Filtering: Warning filters for clean test output

Test Data Validation

The test suite validates:

✅ All 30+ tools function correctly with mock and real data
✅ Property filtering logic handles edge cases
✅ Search functionality is case-insensitive and comprehensive
✅ Agent performance calculations are accurate
✅ Market analysis tools process data correctly
✅ Client matching algorithms work as expected
✅ Area intelligence aggregates data properly
✅ Resource endpoints return valid JSON
✅ Prompt templates generate proper instructions
✅ Error handling for missing or invalid data
✅ Data refresh and caching mechanisms
✅ System statistics and summaries

Continuous Integration

For CI/CD pipelines, use:

# Basic test run pytest tests/ --tb=short # With coverage for CI reporting pytest tests/ --cov=. --cov-report=xml --cov-report=term-missing # Specific test categories for staged testing pytest tests/unit/ --tb=short # Fast unit tests first pytest tests/integration/ --tb=short # Integration tests second

Writing New Tests

When adding new functionality:

Unit Tests: Add to tests/unit/ for core logic
Integration Tests: Add to appropriate tests/integration/test_*.py
Use Fixtures: Leverage existing fixtures in conftest.py
Mock External Dependencies: Use unittest.mock for isolation
Test Edge Cases: Include boundary conditions and error scenarios
Follow Naming Convention: test_*.py files, Test* classes, test_* methods

🛠️ Development

Adding New Tools

Choose appropriate category in tools/
Add tool function with @mcp.tool() decorator
Register in the category's register_*_tools() function
Import and call registration in main.py
Add Tests: Create corresponding tests in tests/integration/

Adding New Resources

Choose appropriate domain module in resources/ (property, agent, market, client, location)
Add resource function with @mcp.resource() decorator and URI pattern
Register in the domain's register_*_resources() function
Import and call registration in main.py
Add Tests: Include resource tests in tests/integration/test_resources.py

Adding New Prompts

Choose appropriate category in prompts/ (property, client, market, or agent)
Add prompt function with @mcp.prompt() decorator
Include parameter defaults and comprehensive instructions
Register in the category's register_*_prompts() function
Add Tests: Include prompt tests in tests/integration/test_prompts.py

Adding New Prompt Categories

Create new file in prompts/ directory (e.g., prompts/new_category_prompts.py)
Follow the existing pattern with register_new_category_prompts(mcp) function
Import and register in prompts/__init__.py
Add Tests: Create corresponding test fixtures and test methods

🎯 Transport Selection Guide

Choose the right transport mode for your use case:

Feature	STDIO (Default)	SSE (Remote)
Best For	Claude Desktop, local tools	Web apps, remote access, team sharing
Latency	~1ms (ultra-fast)	10-100ms (network-dependent)
Multiple Clients	❌ Single client	✅ Multiple simultaneous clients
Remote Access	❌ Local only	✅ Network access
Setup Complexity	✅ Simple (no config)	⚠️ Requires port/URL management
Claude Desktop	✅ Native support	⚠️ Requires mcp-remote proxy
Auto-cleanup	✅ Automatic	⚠️ Manual management
Security	✅ Process isolation	⚠️ Needs authentication

Quick Decision Matrix

Use STDIO when:

Installing in Claude Desktop (recommended)
Running on the same machine as the client
You want the fastest response times
You need simple setup with no configuration

Use SSE when:

Deploying to a remote server or cloud
Multiple team members need access
Building web applications
You need real-time streaming updates

🚀 Publishing to Repository

Recommended Approach

The server is designed to work best with STDIO transport for the MCP ecosystem, making it:

✅ Easy to install: mcp install main.py
✅ Compatible with Claude Desktop: Standard integration
✅ Discoverable: Can be listed in MCP server directories
✅ Simple to use: No port configuration needed
✅ Flexible: SSE mode available for advanced use cases

For End Users

Primary use case (98% of users):

git clone https://github.com/agentic-ops/real-estate-mcp.git cd real-estate-mcp pip install -r requirements.txt mcp install main.py --name "Real Estate"

Advanced use case (remote deployment):

python main.py sse # Run on server # Configure clients to connect to http://your-server:8000/sse

📝 License

This project is licensed under the MIT License.

🤝 Contributing

Fork the repository
Create a feature branch
Add your component following the established patterns
Test thoroughly
Submit a pull request

📖 Further Reading

For a comprehensive deep dive into the architecture, design principles, and real-world applications of this MCP server, read the detailed blog post:

🔌 MCP Servers - Model Context Protocol Implementation

The blog post covers:

Understanding MCP Servers and their business impact
Architecture deep dive with code examples
MCP Tools, Prompts, and Resources explained
Real-world usage scenarios and implementation patterns
Security considerations and best practices
Future implications of MCP technology

Built with the Model Context Protocol (MCP) for seamless AI integration