MCP-FinTechCo

# MCP-FinTechCo Server - Implementation Plan ## Project Overview This project implements a Model Context Protocol (MCP) server using FastMCP 2.0. MCP is a standardized protocol for connecting large language models to external tools and data sources. The server is deployed on Google Cloud Platform (GCP) and provides comprehensive financial technology capabilities including real-time market data, technical indicators, foreign exchange rates, and cryptocurrency pricing. ## Technical Specifications - **Framework**: FastMCP 2.0 - **Language**: Python 3.11 - **Deployment**: GCP e2-small VM in us-central1 region - **Version Control**: GitHub repository (MCP-FinTechCo) - **Documentation**: https://gofastmcp.com ## Implementation Plan ### Phase 1: Project Foundation 1. **Project Structure Setup** - [x] Create `requirements.txt` with FastMCP 2.0, httpx, and python-dotenv - [x] Create `.gitignore` for Python projects - [x] Create `.env.sample` with placeholder environment variables - [x] Create `plan.md` (this document) - [ ] Implement core `server.py` file 2. **Version Control Initialization** - [ ] Initialize local git repository - [ ] Create comprehensive README.md - [ ] Create GitHub repository using `gh repo create` - [ ] Push initial commit to GitHub ### Phase 2: Core Implementation 3. **MCP Server Development** - [ ] Set up FastMCP 2.0 server in `server.py` - [ ] Configure server for both local and production environments - [ ] Implement proper error handling and logging 4. **Initial Tool: Weather Information** - [ ] Implement `get_city_weather` tool - [ ] Integrate with Open-Meteo API (no API key required) - [ ] Add input validation and error handling - [ ] Document tool parameters and return values 5. **Local Testing Infrastructure** - [ ] Create `test_client.py` for MCP server testing - [ ] Implement test cases for `get_city_weather` - [ ] Add usage examples and documentation - [ ] Validate server functionality end-to-end ### Phase 3: Deployment to GCP 6. **GCP Configuration** - [ ] Create `DEPLOYMENT.md` with detailed deployment instructions - [ ] Document gcloud CLI commands for: - Creating e2-small VM instance in us-central1 - Configuring firewall rules - Setting up SSH keys - [ ] Create `startup-script.sh` for VM initialization - [ ] Create systemd service file (`mcp-server.service`) for auto-start 7. **Deployment Automation** - [ ] Create `deploy.sh` script for automated deployment - [ ] Include steps for: - Python 3.11 installation - Virtual environment setup - Dependency installation - Environment variable configuration - MCP server service start ### Phase 4: Expansion (Post-Launch) 8. **Additional Tools and Features** - [ ] Identify and prioritize new tools based on initial feedback - [ ] Implement additional MCP tools - [ ] Update documentation and tests - [ ] Deploy updates to production ## Expected Deliverables 1. **Code** - Fully functional MCP server with `get_city_weather` tool - Local test client with examples - Deployment automation scripts 2. **Documentation** - README.md (setup, usage, API reference) - plan.md (this file) - DEPLOYMENT.md (GCP deployment guide) - Inline code documentation 3. **Configuration** - Environment variable templates (.env.sample) - GCP deployment configurations - Systemd service files 4. **Repository** - GitHub repository (MCP-FinTechCo) - Version controlled with clear commit history - Ready for collaboration and continuous deployment ## Success Criteria - MCP server successfully responds to `get_city_weather` requests locally - Server deploys to GCP without errors - Comprehensive documentation enables easy setup and usage - Test client validates all core functionality - Repository structure supports future expansion ## Next Steps After completing the initial implementation: 1. Conduct thorough local testing 2. Deploy to GCP staging environment 3. Perform production validation 4. Gather feedback for tool expansion 5. Plan and implement additional FinTech-focused tools ## Resources - FastMCP Documentation: https://gofastmcp.com/getting-started/welcome - FastMCP Quickstart: https://gofastmcp.com/getting-started/quickstart - Open-Meteo API: https://open-meteo.com/ - GCP Documentation: https://cloud.google.com/docs