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Real Estate MCP Server

by agentic-ops
GitHub
Python
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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

  1. Clone the repository:

    git clone https://github.com/agentic-ops/real-estate-mcp.git cd real-estate-mcp

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the server:

    python main.py

🔍 MCP Inspector

To inspect and debug your MCP server, you can use the MCP Inspector tool:

npx @modelcontextprotocol/inspector

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

🌐 Server Transport

The server uses Server-Sent Events (SSE) transport, making it compatible with:

  • Web browsers and HTTP clients

  • Traditional MCP clients

  • Custom integrations

Connection Details

  • 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

MCP Client Examples

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

# 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"}}}'

🧪 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

🔄 Benefits of SSE Transport

  • Web Compatible: Direct browser integration

  • Real-time: Server-sent events for live updates

  • HTTP Standard: Works with standard HTTP tools

  • Firewall Friendly: Uses standard HTTP port

  • Scalable: Supports multiple concurrent connections

📝 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

📖 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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

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

  1. 🏗️ Architecture
    1. 🚀 Features
      1. MCP Capabilities
      2. Tool Categories
      3. Resources
      4. Prompts (11 total)
    2. 📦 Installation
      1. 🔍 MCP Inspector
        1. 🌐 Server Transport
          1. Connection Details
          2. Web Client Example
        2. 🔧 Component Details
          1. Core Components
          2. Tool Modules
          3. Resource Modules
          4. Prompt Templates
        3. 📊 Data Structure
          1. 🔍 Usage Examples
            1. MCP Client Examples
          2. 🧪 Testing
            1. Test Structure
            2. Test Categories
            3. Running Tests
            4. Test Features
            5. Test Data Validation
            6. Continuous Integration
            7. Writing New Tests
          3. 🛠️ Development
            1. Adding New Tools
            2. Adding New Resources
            3. Adding New Prompts
            4. Adding New Prompt Categories
          4. 🔄 Benefits of SSE Transport
            1. 📝 License
              1. 🤝 Contributing
                1. 📖 Further Reading

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