What is Mcp Mortgage Server

The mcp-mortgage-server is a FastAPI-based server designed for parsing Loan Estimate (LE) and Closing Disclosure (CD) PDFs into MISMO-compliant JSON. It aims to facilitate AI-driven mortgage automation, compliance, and decision-making.

Use cases

Use cases include automating the parsing of mortgage documents for compliance audits, integrating with AI agents for enhanced decision-making, and comparing different mortgage offers based on parsed data.

How to use

To use the mcp-mortgage-server, clone the repository from GitHub, set up a virtual environment, and run the server. The server provides a standardized API for mortgage document parsing and comparison tools.

Key features

Key features include API key authentication, rate limiting support, CORS middleware configuration, and integrations with AI frameworks like CrewAI, AutoGen, and LangChain. It also has an extensible architecture for adding mortgage parsing tools.

Where to use

The mcp-mortgage-server is suitable for use in the mortgage industry, particularly in areas involving document processing, compliance checks, and automated decision-making.

MCP Server (Mortgage Comparison Platform)

A FastAPI-based server that provides mortgage document parsing and comparison tools through a standardized API. The server is designed to be easily integrated with various AI frameworks including CrewAI, AutoGen, and LangChain.

Currently implements a basic “hello” tool as a proof of concept, with mortgage document parsing tools coming soon.

Status

This is a beta release (v0.1.0) that provides:

• Core server infrastructure with security features
• Basic “hello” tool for testing framework integrations
• Example integrations with CrewAI, AutoGen, and LangChain

Future versions will add mortgage document parsing and comparison tools.

Features

• FastAPI server with production-ready features:
  • API key authentication
  • Rate limiting support
  • CORS middleware configuration
• Framework integrations for AI agents:
  • CrewAI
  • AutoGen
  • LangChain
• Extensible architecture for adding mortgage parsing tools
• Open source for transparency and community contributions

Quick Start

1. Clone the repository:

   git clone https://github.com/confersolutions/mcp-mortgage-server.git
   cd mcp-mortgage-server
   

Roadmap

• ✅ Core server infrastructure with security and rate limiting
• ✅ Framework integrations (CrewAI, AutoGen, LangChain)
• ✅ Basic tool implementation (“hello” endpoint)
• 🚧 Loan Estimate (LE) parsing to MISMO format
• 🚧 Closing Disclosure (CD) parsing
• 🚧 Mortgage comparison tools
• 🚧 Additional mortgage document analysis features

Installation

1. Clone the repository
2. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies:

   pip install fastapi uvicorn slowapi python-dotenv
   pip install crewai autogen langchain langchain-openai
   

Configuration

Create a .env file in the root directory with the following variables:

API_KEY=your_api_key_here
RATE_LIMIT_PER_MINUTE=120
ALLOWED_ORIGINS=http://localhost:3000
HOST=0.0.0.0
PORT=8001
WORKERS=1

Running the Server

python server.py

The server will start on http://localhost:8001 by default.

API Endpoints

Health Check

GET /health
Response: {"status": "healthy"}

List Available Tools

GET /tools
Headers: X-API-Key: your_api_key_here
Response: List of available tools and their configurations

Call Tool

POST /call
Headers: X-API-Key: your_api_key_here
Body: {
    "tool": "hello",
    "input": {
        "name": "World"  // Optional
    }
}
Response: {
    "output": "Hello, World!"
}

Framework Integration Examples

See examples/test_all_integrations.py for examples of how to use the server with:

• CrewAI
• AutoGen
• LangChain

CrewAI Example

from crewai import Agent, Task, Crew
from mcp_toolkit import MCPToolkitCrewAI

toolkit = MCPToolkitCrewAI()
tools = await toolkit.get_tools()

agent = Agent(
    role="Greeter",
    goal="Say hello to the user",
    tools=tools
)

task = Task(
    description="Say hello to the user",
    agent=agent
)

crew = Crew(
    agents=[agent],
    tasks=[task]
)

result = await crew.kickoff()

AutoGen Example

from autogen import AssistantAgent, UserProxyAgent
from mcp_toolkit import MCPToolkitAutoGen

toolkit = MCPToolkitAutoGen()
tools = await toolkit.get_tools()

assistant = AssistantAgent(
    name="assistant",
    llm_config={"tools": tools}
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"use_docker": False}
)

await user_proxy.initiate_chat(assistant, message="Please say hello to Alice")

LangChain Example

from langchain.agents import Tool, AgentExecutor, create_react_agent
from langchain_openai import ChatOpenAI
from mcp_toolkit import MCPToolkitLangChain

toolkit = MCPToolkitLangChain()
tools = [
    Tool(
        name="hello",
        func=lambda x: asyncio.get_event_loop().run_until_complete(toolkit.hello(name=x)),
        description="A tool that says hello to someone",
        return_direct=True
    )
]

llm = ChatOpenAI(temperature=0)
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

result = await agent_executor.ainvoke({"input": "Please say hello to Bob"})

Rate Limiting

The server implements rate limiting using slowapi. By default, it’s set to 120 requests per minute per IP address. This can be configured using the RATE_LIMIT_PER_MINUTE environment variable.

Security

• API key authentication is required for all endpoints except /health
• CORS is configured to allow specific origins (set via ALLOWED_ORIGINS environment variable)
• All exceptions are caught and returned with appropriate error messages

Contributing

Feel free to open issues or submit pull requests for improvements.

About

This project is maintained by Confer Solutions. For questions or support, contact us at [email protected].

License

MIT License - see LICENSE file for details.