Financial Report

Learn MCP by financial report generator demo

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

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.

How to use

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.

How to install

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.txt

Step 2: Run the MCP server.

python -m src.mcp_server.server

Step 3: (Optional) If you want to use the AI-enhanced features, install and start a local Ollama service and ensure the model is available.

AI features

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.

Troubleshooting

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.

Available tools

company_report_generator

Generates financial reports for individual companies by pulling and processing data, then formatting a readable report.

financial_data

Provides tools to fetch and process financial data from internal sources or external APIs.

report_generator

Assembles processed data into final report documents in the requested format.

ai_enhanced_report_generator

Enables AI-driven analysis and report generation, leveraging a local AI model when available.

ollama_client

Facilitates communication with a locally deployed Ollama AI model for smart analyses.