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Learn MCP by financial report generator demo
Configuration
View docs{
"mcpServers": {
"kennanyang-financial-report": {
"command": "python",
"args": [
"-m",
"src.mcp_server.server"
]
}
}
}You will learn to build and run an MCP-based financial report generator that can create interactive company reports, leverage AI analysis, and run locally with simulated data or real APIs once wired up. This server focuses on MCP tooling, data handling, and optional AI integration with local models, helping you learn end-to-end MCP workflows.
You operate the financial report MCP server through a client that communicates with the server using MCP. You can start the server, then generate reports for individual companies or in batches, and optionally enable AI-assisted analysis when you have a local AI model available. When you run the interactive demos, you will choose the function, select a company, pick a report type, and watch the server generate a detailed financial report.
Prerequisites: ensure Python 3.8+ is installed on your system. You may also want a local AI runtime if you plan to use AI features.
Step 1: Install Python dependencies.
pip install -r requirements.txtStep 2: Run the MCP server.
python -m src.mcp_server.serverStep 3: (Optional) If you want to use the AI-enhanced features, install and start a local Ollama service and ensure the model is available.
The server optionally integrates a local AI model to provide smart financial analysis and investment insights. You can switch between local AI (Ollama) and, if configured, external AI services. The AI features support company analysis, AI-generated reports, and investment guidance.
Prerequisites for AI features include having Ollama installed and the deepseek-r1:7b model available locally.
If you encounter import or runtime errors, verify you are executing commands from the project root and that all dependencies are installed.
If the server cannot start, check that Python is accessible and that the module path matches src.mcp_server.server.
Generates financial reports for individual companies by pulling and processing data, then formatting a readable report.
Provides tools to fetch and process financial data from internal sources or external APIs.
Assembles processed data into final report documents in the requested format.
Enables AI-driven analysis and report generation, leveraging a local AI model when available.
Facilitates communication with a locally deployed Ollama AI model for smart analyses.