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/

🚀 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:

1. Restart Claude Desktop after configuring the server
2. Look for the MCP indicator (🔨 hammer icon) in the bottom-right corner
3. Click the indicator to see available tools and resources
4. 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 search_properties tool and reads the realestate://properties/area/Downtown Riverside resource to show you all matching properties.

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

Claude: Reads the realestate://agent/AG001/dashboard resource to display comprehensive agent metrics.

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

Claude: Uses the filter_properties tool with your criteria and presents matching listings.

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):

1. Start server: python main.py sse
2. 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:

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

🛠️ Development

Adding New Tools

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

Adding New Resources

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

Adding New Prompts

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

Adding New Prompt Categories

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

🎯 Transport Selection Guide

Choose the right transport mode for your use case:

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:

1. ✅ Easy to install: mcp install main.py
2. ✅ Compatible with Claude Desktop: Standard integration
3. ✅ Discoverable: Can be listed in MCP server directories
4. ✅ Simple to use: No port configuration needed
5. ✅ 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

1. Fork the repository
2. Create a feature branch
3. Add your component following the established patterns
4. Test thoroughly
5. Submit a pull request

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📖 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

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