loan-risk-mcp

demo project only. no production credit risk logic

Loan & Risk Assessment MCP Server

A small Model Context Protocol (MCP) server built for a Digital Banking Assistant Copilot Studio agent. It exposes three tools the agent can call when it needs real computation instead of guessing:

Disclaimer: This is a portfolio/demo project. The risk logic is a transparent toy rule set, not a real credit model. Any production use would require fair-lending, compliance, and model-risk review.

Architecture

User ── chats with ──▶ Copilot Studio Agent ("Digital Banking Assistant")
                              │
                              │  calls tool over MCP (Streamable HTTP)
                              ▼
                    Loan & Risk Assessment MCP Server (this repo)
                              │
                              ▼
                 Plain-language result returned into the conversation

The MCP server is a tool the agent reaches for mid-conversation — it's part of the same Digital Banking Assistant project, not a separate demo.

Related MCP server: MCP Mortgage Server

Project structure

loan-risk-mcp/
├── server.py         # The MCP server + 3 tools
├── test_tools.py      # Direct unit tests of the tool logic
├── requirements.txt    # Pinned dependency versions
└── README.md

Setup

python3 -m venv venv
source venv/bin/activate        # venv\Scripts\activate on Windows
pip install -r requirements.txt

Testing the logic

Run the tool functions directly (no MCP transport involved) to confirm the math is right before wiring anything up:

python test_tools.py

Running the server

Local / stdio (for local MCP clients, e.g. Claude Desktop, MCP Inspector):

python server.py

HTTP (required for a cloud client like Copilot Studio to reach it):

python server.py --http --port 8000

You can sanity-check the HTTP server with the official MCP Inspector:

npx @modelcontextprotocol/inspector
# then point it at http://localhost:8000/mcp

Example call and response

Request: calculate_dti(monthly_income=5000, monthly_debts=1000)

{
  "dti_ratio_percent": 20.0,
  "band": "good",
  "explanation": "DTI of 20.0% is at or below the common 35% affordability threshold. Generally considered healthy."
}

Notes

• This demonstrates pro-code (Python MCP server) + low-code (Copilot Studio topics) working together — the direction the platform is actively moving toward with connected agents and MCP.

• The risk tool is deliberately rules-based and explainable rather than a black-box ML score, which mirrors how banks actually need AI outputs to be auditable.

• Call out clearly in interviews that this is a demo: real underwriting logic requires far more rigor, data, and compliance sign-off.