Real Estate MCP
DEMOComprehensive real estate data management with 30+ tools, property search, market analysis, and client management.
edwin.genego.io MCP
PLANNEDMy entire website, including the blog, portfolio, and contact form turned into a MCP server.
Understanding MCP Servers
Here’s what I’ve discovered about building AI systems: the quality of AI output is fundamentally limited by the quality of context it receives. You can have the most sophisticated model in the world, but if it doesn’t know about your specific business data, customer history, or real-time market conditions, it’s essentially flying blind.
Most teams try to solve this through prompt engineering—cramming as much relevant information as possible into the initial prompt. But this approach hits hard limits quickly:
- Token constraints: Even the largest context windows can’t hold your entire business database
- Static data: Prompts can’t access live, changing information
- No persistence: Each conversation starts from scratch
- Security concerns: Sensitive data has to be passed through prompts
This is where Model Context Protocol (MCP) servers change everything. Instead of trying to stuff context into prompts, MCP servers give AI models direct, secure access to the data and systems they need to be genuinely useful.
MCP servers work as a translator and gatekeeper between AI models and your business systems. It’s not just a database connection—it’s a sophisticated middleware layer that:
Provides Structured Access: Instead of raw database queries, MCP servers expose business-friendly tools like “find_properties_in_budget” or “check_client_preferences”
Maintains Security: All data access goes through defined permissions and authentication layers
Enables Real-Time Data: AI can access live inventory, current market prices, or up-to-date calendar availability
Preserves Context: Sessions can maintain state across multiple interactions
Standardizes Integration: One protocol works across different AI models and platforms
The Core Insight
Architecture Deep Dive
The most effective MCP architecture is simple. Here’s how the pieces fit together:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────────┐
│ AI Model │ │ MCP Server │ │ Business Systems │
│ (Claude, │◄──►│ (Protocol │◄──►│ (Databases, APIs, │
│ GPT, etc.) │ │ Translation) │ │ Files, Services) │
└─────────────────┘ └──────────────────┘ └─────────────────────┘
│ │ │
│ │ │
▼ ▼ ▼
Natural Language Structured Tools Raw Data Access
Requests & Resources & System Calls
The AI Model makes requests in natural language: “Show me all properties under $500k in downtown with 2+ bedrooms”
The MCP Server translates this into structured tool calls: search_properties(max_price=500000, location="downtown", min_bedrooms=2)
Business Systems return structured data which the MCP server formats for the AI model to understand and act upon.
MCP Tools: AI-Callable Functions
MCP Tools are the core interface between AI models and your business logic. These are different from traditional APIs—they’re designed specifically for AI consumption with rich semantic descriptions and structured schemas.
What makes MCP tools effective:
- AI-Optimized Descriptions: Each tool includes natural language descriptions that help AI models understand when and how to use them
- Structured Input Schemas: JSON Schema validation ensures AI provides correctly formatted parameters
- Semantic Categorization: Tools are grouped by business function, not technical implementation
- Rich Return Data: Tools return structured data that AI can interpret and act upon
@mcp.tool()
def search_properties(
query: str = "",
max_price: Optional[int] = None,
min_bedrooms: Optional[int] = None,
area: Optional[str] = None
) -> str:
"""
Search for properties matching specific criteria.
This tool allows you to find properties based on text search,
price range, bedroom count, and geographic area. Perfect for
helping clients find their ideal home.
"""
# Business logic implementation
results = data_manager.search_properties(query)
if max_price:
results = [p for p in results if p.get("price", 0) <= max_price]
return json.dumps({
"properties": results,
"total_found": len(results),
"search_criteria": {
"query": query,
"max_price": max_price,
"min_bedrooms": min_bedrooms,
"area": area
}
}, indent=2)Key differences from regular APIs:
| Traditional API | MCP Tool |
|---|---|
| Technical endpoint names | Business-friendly function names |
| Minimal documentation | Rich AI-readable descriptions |
| Raw data responses | Contextual, structured responses |
| Fixed parameter types | Flexible, optional parameters |
| Error codes | Natural language error explanations |
MCP Prompts: AI Guidance Templates
MCP Prompts are user-controlled templates that guide AI analysis and decision-making. These are fundamentally different from system prompts—they’re dynamic, parameterized instructions that users can invoke and customize.
What makes MCP prompts useful:
- User-Controlled: Unlike system prompts, these are explicitly invoked by users
- Parameterized: Accept dynamic inputs to customize analysis
- Domain-Specific: Tailored to specific business contexts and workflows
- Reusable: Templates that can be applied across different data sets
@mcp.prompt()
def property_investment_analysis(
property_id: str,
investment_horizon: str = "5 years",
risk_tolerance: str = "moderate"
) -> str:
"""
Generate comprehensive investment analysis for a property.
Analyzes market trends, rental potential, appreciation prospects,
and risk factors based on current market data and comparable sales.
"""
property_data = data_manager.get_property_insights(property_id)
market_data = data_manager.get_area_market_data(property_data["area"])
return f"""
# Property Investment Analysis
## Property Overview
Analyze this property for investment potential:
{json.dumps(property_data, indent=2)}
## Market Context
Consider these market conditions:
{json.dumps(market_data, indent=2)}
## Analysis Framework
**Investment Horizon**: {investment_horizon}
**Risk Tolerance**: {risk_tolerance}
Please provide a comprehensive analysis covering:
1. **Cash Flow Analysis**
- Estimate rental income potential
- Calculate operating expenses and taxes
- Project monthly cash flow
2. **Appreciation Potential**
- Analyze recent sales trends in the area
- Consider neighborhood development plans
- Evaluate long-term growth prospects
3. **Risk Assessment**
- Market volatility factors
- Property-specific risks
- Economic sensitivity analysis
4. **Comparative Analysis**
- Compare to similar investment properties
- Benchmark against market averages
- Alternative investment considerations
5. **Recommendation**
- Investment score (1-10)
- Key decision factors
- Optimal exit strategy
Base your analysis on the provided data and current market conditions.
"""MCP Prompt vs. System Prompt:
| System Prompt | MCP Prompt |
|---|---|
| Fixed at model initialization | Dynamically invoked by users |
| Controls general behavior | Guides specific analysis tasks |
| Hidden from users | Transparent and customizable |
| Model-wide application | Context-specific application |
| Technical configuration | Business workflow integration |
The Power of Tool + Prompt Combinations
What works particularly well is when MCP tools and prompts work together. Here’s a practical example:
# User invokes prompt
prompt_result = mcp.call_prompt(
"property_investment_analysis",
property_id="PROP123",
investment_horizon="10 years",
risk_tolerance="aggressive"
)
# AI model receives the prompt and automatically calls relevant tools
tools_used = [
"get_property_insights", # Detailed property data
"get_area_market_data", # Local market trends
"calculate_rental_potential", # Income projections
"get_comparable_sales", # Market comparisons
"analyze_neighborhood_trends" # Growth prospects
]
# Result: Comprehensive analysis based on live dataThis creates what I call guided intelligence workflows where:
- Prompts provide the analytical framework and domain expertise
- Tools supply the real-time data and business logic
- AI models synthesize everything into actionable insights
The combination transforms AI from a generic text generator into a domain-specific expert with access to your business systems and guided by your analytical frameworks.
MCP Inspector: Development & Debugging Tool
Building MCP servers requires the right development tools. The MCP Inspector is an essential debugging and exploration tool that provides a visual interface for testing MCP servers during development.
Essential Development Tool
Key Inspector Features
🔗 Resources Tab: Browse and inspect all available resources, both static data and dynamic templates. Perfect for verifying your resource implementations and understanding data structures.
🛠️ Tools Tab: Test all available tools with parameter validation and real-time execution. Essential for debugging tool logic and verifying return data formats.
📝 Prompts Tab: Explore and test prompt templates with parameter substitution. Crucial for validating prompt logic and testing different parameter combinations.
Development Workflow
The MCP Inspector integrates seamlessly into your development workflow:
- Start your MCP server (e.g.,
python main.py) - Launch MCP Inspector and connect to your server endpoint
- Explore resources to verify data structures and availability
- Test tools with various parameters to ensure correct behavior
- Validate prompts with different argument combinations
- Monitor history to debug execution flows and identify issues
Connection Example:
# Your MCP server running on SSE transport
Transport Type: SSE
URL: http://localhost:8000/sse
# Inspector connects and shows all available capabilities
✅ Connected - 30 tools, 10 resources, 5 prompts discoveredThis visual debugging approach dramatically speeds up MCP server development and helps ensure robust implementations before deploying to production AI systems.
Real Estate MCP Server Demo {#real-estate-demo}
Let me show you what this looks like in practice. This comprehensive real estate example demonstrates the full potential of connected AI systems. This is a production MCP server I’ve made available as open source on GitHub.
Live Demo MCP Server
Production ReadyArchitecture Overview
This modular design pattern works well for MCP servers:
real-estate-mcp/
├── main.py # FastMCP server entry point
├── utils.py # Core data management utilities
├── tools/ # 30+ 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/ # 10 MCP Resources (static and templates)
│ ├── static_resources.py # Static data resources
│ └── resource_templates.py # Dynamic resource templates
├── prompts/ # 5 MCP Prompts (user-controlled templates)
│ └── prompt_templates.py # Analysis and reporting prompts
└── data/ # Real estate data files
├── properties/ # Property listings and details
├── agents/ # Agent profiles and performance
├── clients/ # Client database and preferences
├── market/ # Market analytics and trends
├── transactions/ # Recent sales and transactions
├── areas/ # Area information and demographics
└── amenities/ # Local amenities and services
Key Features Demonstrated
🏠 Property Management (7 tools)
- Advanced property search with multiple criteria
- Property insights with market context
- Area-based property listings
- Comparable sales analysis
👥 Agent Operations (6 tools)
- Agent performance dashboards
- Client portfolio management
- Sales tracking and analytics
- Specialization matching
📊 Market Analysis (7 tools)
- Real-time market overview
- Area-specific performance metrics
- Investment opportunity analysis
- Comparative market analysis
🤝 Client Management (3 tools)
- Client preference matching
- Budget-based recommendations
- Personalized property suggestions
🏘️ Area Intelligence (9 tools)
- Comprehensive area reports
- School district information
- Amenities and demographics
- Transportation and accessibility
Technical Implementation
The server uses Server-Sent Events (SSE) transport, which I’ve found works well for teams because it’s compatible with web browsers and HTTP clients:
# Start the MCP server
python main.py
# Server runs on http://127.0.0.1:8000/sse
# Ready for MCP client connectionsConnection endpoints:
- SSE Endpoint:
http://127.0.0.1:8000/sse(for establishing connection) - Message Endpoint:
http://127.0.0.1:8000/messages/(for posting MCP messages)
Real-World Usage Example
Here’s how an AI model would interact with this MCP server:
# Search for 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 home with garden"}}}'
# Get comprehensive area report
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": "get_comprehensive_area_report", "arguments": {"area": "Downtown Riverside"}}}'
# Match client to properties
curl -X POST http://127.0.0.1:8000/messages/ \
-H "Content-Type: application/json" \
-d '{"jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": {"name": "match_client_preferences", "arguments": {"client_id": "CLI001"}}}'Why This Matters
This MCP server demonstrates how AI models can access structured, business-specific data in real-time. Instead of generic responses, the AI can provide:
- Personalized property recommendations based on client preferences and budget
- Market insights with current pricing trends and investment opportunities
- Comprehensive area analysis including schools, amenities, and demographics
- Agent performance data for informed decision-making
The server is designed as both a functional tool and a reference implementation for enterprise MCP servers.
Implementation Architecture
Building MCP servers involves these three core components:
1. Protocol Layer
from mcp import Server, McpError
from mcp.types import Tool, TextContent
server = Server("real-estate-mcp")
@server.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="search_properties",
description="Search properties by criteria",
inputSchema={
"type": "object",
"properties": {
"max_price": {"type": "number"},
"location": {"type": "string"},
"bedrooms": {"type": "number"}
}
}
)
]2. Business Logic Layer
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "search_properties":
properties = await property_service.search(
max_price=arguments.get("max_price"),
location=arguments.get("location"),
bedrooms=arguments.get("bedrooms")
)
return [TextContent(
type="text",
text=format_property_results(properties)
)]3. Data Integration Layer
class PropertyService:
def __init__(self, db_connection):
self.db = db_connection
async def search(self, max_price=None, location=None, bedrooms=None):
query = "SELECT * FROM properties WHERE 1=1"
params = []
if max_price:
query += " AND price <= ?"
params.append(max_price)
if location:
query += " AND location LIKE ?"
params.append(f"%{location}%")
return await self.db.execute(query, params)MCP Transport Types: STDIO vs SSE
Choosing transport mechanisms for MCP servers is crucial for your deployment architecture. The Model Context Protocol supports different transport types, each optimized for specific use cases and deployment scenarios.
Transport Choice Matters
STDIO Transport (Standard Input/Output)
STDIO is the traditional MCP transport, designed for direct process communication. It’s what most desktop AI applications like Claude Desktop use.
How STDIO Works:
# AI client launches MCP server as subprocess
claude-desktop → spawns → python mcp-server.py
↓
stdin/stdout pipes
↓
JSON-RPC messages over pipesSTDIO Characteristics:
✅ Advantages:
- Zero network overhead - Direct process communication
- Built-in security - No network exposure by default
- Simple deployment - Single executable or script
- Low latency - Direct pipe communication
- Resource efficiency - No HTTP server overhead
❌ Limitations:
- Desktop only - Requires process spawning capability
- Single client - One AI model per server instance
- No web access - Cannot be accessed from browsers
- Local deployment - Must run on same machine as AI client
- Limited debugging - Harder to inspect with external tools
STDIO Configuration Example:
{
"mcpServers": {
"real-estate": {
"command": "python",
"args": ["mcp-server.py"],
"env": {
"DATA_PATH": "/path/to/real-estate-data"
}
}
}
}SSE Transport (Server-Sent Events)
SSE (Server-Sent Events) is a web-based transport that runs MCP servers as HTTP services. This is what I chose for the Real Estate MCP server to enable broader accessibility and easier development.
How SSE Works:
# MCP server runs as HTTP service
python main.py → HTTP server on :8000
↓
/sse endpoint (connection)
/messages endpoint (communication)
↓
JSON-RPC over HTTP + SSE streamsSSE Characteristics:
✅ Advantages:
- Web compatible - Works with browsers and web apps
- Multiple clients - Can serve multiple AI models simultaneously
- Remote access - Deploy anywhere, access from anywhere
- Easy debugging - Use MCP Inspector, curl, or browser dev tools
- Cloud deployment - Perfect for containerized environments
- Development friendly - Hot reload, live testing, REST-like debugging
❌ Limitations:
- Network overhead - HTTP protocol adds some latency
- Security considerations - Requires proper authentication/authorization
- Resource usage - HTTP server consumes more memory
- Complexity - More moving parts than STDIO
SSE Server Example:
# FastMCP with SSE transport
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Real Estate MCP")
@mcp.tool()
def search_properties(query: str) -> str:
"""Search for properties matching criteria"""
return json.dumps(property_search_results)
# Runs HTTP server with SSE endpoints
if __name__ == "__main__":
mcp.run(transport="sse", port=8000)Transport Comparison
| Aspect | STDIO | SSE |
|---|---|---|
| Deployment | Local process | HTTP service |
| Client Support | Desktop AI apps | Web apps + Desktop |
| Debugging | Process inspection | HTTP tools + Inspector |
| Scalability | Single client | Multiple clients |
| Development | Restart required | Hot reload possible |
| Security | Process isolation | Network authentication |
| Latency | Ultra-low | Low (HTTP overhead) |
| Use Case | Production desktop | Development + Web |
Why I Chose SSE for the Real Estate MCP
For the Real Estate MCP server, I chose SSE transport for several strategic reasons:
🌐 Development Experience: SSE enables using the MCP Inspector for visual debugging, making development significantly faster and more intuitive.
🔧 Testing & Validation: HTTP endpoints allow easy testing with curl, Postman, or browser tools, enabling rapid iteration during development.
☁️ Deployment Flexibility: SSE servers can be deployed as containerized services, making them perfect for cloud environments and CI/CD pipelines.
📊 Multi-Client Support: A single SSE server can serve multiple AI models or applications simultaneously, improving resource utilization.
🔍 Observability: HTTP-based transport makes it easier to add logging, monitoring, and analytics to understand server usage patterns.
Choosing the Right Transport
Use STDIO when:
- Building for desktop AI applications (Claude Desktop, etc.)
- Maximum performance is critical
- Single-client scenarios
- Simple deployment requirements
- Security through process isolation is preferred
Use SSE when:
- Developing and debugging MCP servers
- Building web-based AI applications
- Need multi-client support
- Deploying to cloud environments
- Want HTTP-compatible tooling and monitoring
Both transports implement the same MCP protocol, so you can develop with SSE for easier debugging and then deploy with STDIO for production desktop integration if needed.
Security and Access Control
Implementing MCP servers requires careful attention to security since these servers handle sensitive business data:
Authentication: Each tool call includes authentication tokens and user context Authorization: Role-based access controls determine what data each user can access Audit Logging: All interactions are logged for compliance and debugging Data Encryption: Sensitive data is encrypted both in transit and at rest Rate Limiting: Prevents abuse and ensures system stability
@server.call_tool()
async def call_tool(name: str, arguments: dict, context: RequestContext) -> list[TextContent]:
# Verify user permissions
if not await auth_service.can_access_tool(context.user_id, name):
raise McpError(f"Access denied for tool: {name}")
# Log the request
await audit_service.log_tool_call(
user_id=context.user_id,
tool_name=name,
arguments=arguments,
timestamp=datetime.utcnow()
)
# Execute with user context
return await execute_tool(name, arguments, context)The Business Impact
The shift to MCP servers represents a fundamental change in how AI can help businesses. Here’s what changes:
Before MCP Servers
- AI answers based on training data and prompt information
- Responses are generic and often outdated
- No access to business-specific data or processes
- Each conversation starts from scratch
- Manual data gathering required for complex queries
After MCP Servers
- AI answers based on live, business-specific data
- Responses are personalized and current
- Direct integration with all business systems
- Persistent context across interactions
- Automated data synthesis and analysis
Real Example: A client asks “What properties do you have that would work for a growing family?”
Without MCP: Generic advice about family-friendly features
With MCP: “Based on your budget of $450k and preference for good schools, I found 3 properties in the Riverside district. The home at 123 Oak Street has 4 bedrooms, a large backyard, and is 0.3 miles from Lincoln Elementary (rated 9/10). Would you like me to check availability for a showing this weekend?”
Future Implications
Across the industry, MCP servers represent a fundamental shift in AI architecture. We’re moving from AI as a tool to AI as a connected participant in business processes.
What’s Coming Next:
Multi-Server Orchestration: AI models coordinating across multiple MCP servers for complex, cross-system workflows
Autonomous Actions: MCP servers that can not just read data but also execute business processes with appropriate approvals
Real-Time Adaptation: Servers that learn from interaction patterns and optimize their tool offerings
Industry Standardization: Common MCP server patterns for different industries (healthcare, finance, legal, etc.)
Getting Started
For building your own MCP server, here’s the recommended approach:
- Start Small: Pick one specific use case and build a focused MCP server
- Define Tools Carefully: Think in terms of business functions, not database operations
- Security First: Build authentication and authorization from day one
- Test Extensively: MCP servers become critical infrastructure—reliability matters
- Document Everything: Other developers will need to understand and extend your tools
The goal isn’t to replace existing systems but to make them accessible to AI in a structured, secure way.
Connection to My AI Journey
This exploration of MCP servers represents another step in my evolution from prompt engineering to model training to now building AI infrastructure. Each phase has taught me something important:
Prompting taught me: AI capabilities are limited by the context you provide
Training showed me: You can modify AI behavior, but it requires significant resources
MCP servers reveal: You can dramatically enhance AI capabilities by giving it access to the right data and tools
The pattern is clear: the most effective AI systems aren’t just about better models—they’re about better integration between AI and the systems that matter to your business.
This post is part of my ongoing exploration of practical AI architecture. You can see more experiments and projects on my projects page, or read about my broader automation philosophy in “How Far Can AI Really Take You?”