Project Description

MCP Traits Matcher

Description

A personality analysis server built using the FastMCP framework. It provides tools and resources for personality analysis and matching. The system analyzes personality traits based on friendliness and dominance scores, allowing you to create persons, define traits, and find matches for job descriptions.

Architecture Overview

The application consists of several key components:

• FastMCP Server: Handles HTTP requests and provides MCP protocol endpoints
• Data Access Objects (DAOs): Manage database interactions for persons and traits
• Pydantic Models: Define data structures and validation
• Tools and Resources: Expose functionality through MCP endpoints

graph TD
    A[FastMCP Server] --> B[Tools: create_person, add_description, etc.]
    A --> C[Resources: persons://all, traits://all]
    B --> D[MCPPersonDAO]
    B --> E[MCPTraitDAO]
    C --> D
    C --> E
    D --> F[SQLite: mcp_persons.db]
    E --> G[SQLite: mcp_traits.db]

Features

• Creates persons and traits with personality scores
• Adds descriptions to persons, updating their personality based on traits
• Finds people matching a company's job description using Euclidean distance
• Exposes resources for listing persons and traits
• RESTful API endpoints for programmatic access

Setup Instructions

Prerequisites

• Python 3.8+
• uv (Python package manager)

Installation

1. Clone the repository: bash git clone <repository-url> cd mcp-traits-matcher

2. Create a virtual environment: bash python -m uv venv .venv

3. Activate the virtual environment:

   • Windows: .venv\Scripts\activate
   • Linux/macOS: source .venv/bin/activate

4. Install dependencies: bash .venv\Scripts\python.exe -m uv pip install -e .[test]

Database Setup

The server uses SQLite databases (mcp_persons.db and mcp_traits.db). These databases will be created automatically when the server is run. You can configure the database paths using environment variables:

export MCP_PERSONS_DB=custom_persons.db
export MCP_TRAITS_DB=custom_traits.db

Configuration

Create a .env file in the project root:

MCP_PERSONS_DB=mcp_persons.db
MCP_TRAITS_DB=mcp_traits.db
LOG_LEVEL=INFO

Usage Examples

Creating a person

result = await mcp.create_person(name="John Doe")
print(result)  # "Person 'John Doe' created."

Adding a description to a person

result = await mcp.add_description(name="John Doe", description="friendly and dominant")
print(result)  # "Description added to person 'John Doe'."

Creating a trait

result = await mcp.create_trait(name="friendly", friendliness=8.0, dominance=2.0)
print(result)  # "Trait 'friendly' created with friendliness: 8.0, dominance: 2.0."

Finding matches for a job description

matches = await mcp.find_matches(
    company_name="Acme Corp",
    job_description="Looking for friendly and dominant candidates"
)
print(matches)  # ["John Doe", "Jane Smith"]

Listing all persons

import requests
response = requests.get("http://localhost:8000/persons://all")
persons = response.json()
print(persons)

API Documentation

Resources

Example Response for persons://all:

[
  {
    "name": "John Doe",
    "friendliness": 7.5,
    "dominance": 3.2
  }
]

Tools

Error Handling

The API returns appropriate HTTP status codes and error messages:

• 400 Bad Request: Invalid input parameters
• 404 Not Found: Person or trait not found
• 500 Internal Server Error: Database or server errors

Development

Running Tests

.venv\Scripts\python.exe -m pytest tests/

Running the Server

.venv\Scripts\python.exe -m src.traits_matcher_server

The server will start on http://localhost:8000

Troubleshooting

Common Issues

1. Database Connection Errors: Ensure the database files are writable and not corrupted
2. Import Errors: Verify all dependencies are installed correctly
3. Port Already in Use: Change the port using environment variable PORT=8001

Debugging

Enable debug logging by setting LOG_LEVEL=DEBUG in your .env file.

Dependencies

• scipy - Scientific computing for distance calculations
• pydantic>=2.7.2,<3.0.0 - Data validation and serialization
• fastmcp - MCP framework
• python-dotenv - Environment variable management
• pytest - Testing framework (dev dependency)

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Run tests: pytest
5. Commit your changes: git commit -m 'Add amazing feature'
6. Push to the branch: git push origin feature/amazing-feature
7. Open a Pull Request

License

This project is licensed under the MIT-0 License - see the LICENSE file for details.

Source Code

View the repository on GitHub.