Which integrations are available for this server?

Enables local LLM inference using the Deepseek-R1 model for financial analysis and natural language processing of stock market queries Provides data processing capabilities for analyzing and manipulating stock market data retrieved from financial sources Supports data validation and schema management for structured financial data and API interactions Core runtime environment enabling the financial analysis agent and MCP server functionality

📈 MCP-powered Financial Analyst using Local Language Model Support

This project implements a financial analysis agent powered using a locally hosted language model (LLM) like Deepseek‑R1 model. It allows you to interactively query and analyze stock market data, with results displayed either:

Inside Cursor IDE via MCP, or
Directly in your terminal using a standalone Python MCP. inside Cursor IDE.

Features:

Multi‑agent orchestration
Local LLM inference with Ollama and Deepseek‑R1
MCP integration conversational analysis
Data fetching and visualization using yfinance, pandas, and matplotlib

Technologies Used:

Ollama – serves the Deepseek-R1 model locally
yfinance – stock market data
pandas – data processing
matplotlib – chart rendering
MCP – conversational commands (Cursor IDE or terminal)

Setup and installations

Install Ollama

# Setting up Ollama on linux curl -fsSL https://ollama.com/install.sh | sh # Pull the Deepseek-R1 model ollama pull deepseek-r1

Install Dependencies

Ensure you have Python 3.12 or later installed.

You can use uv to directly install the required dependencies (recommended).

uv sync

Or you can also use pip to install the following dependencies to your local environment.

pip install ollama mcp pydantic yfinance pandas matplotlib

Running the project

You have two options for running the MCP server.

Option A: Using Cursor IDE MCP

Set up your MCP server as follows:

Go to Cursor settings
Select MCP
Add new global MCP server.

In the JSON file, add this:

{ "mcpServers": { "financial-analyst": { "command": "uv", "args": [ "--directory", "absolute/path/to/project_root", "run", "server.py" ] } } }

You should now be able to see the MCP server listed in the MCP settings.

In Cursor MCP settings make sure to toggle the button to connect the server to the host. Done! Your server is now up and running.

You can now chat with Cursor and analyze stock market data. Simply provide the stock symbol and timeframe you want to analyze, and watch the magic unfold.

Option B: Using Python MCP (standalone terminal)

If you prefer not to use Cursor, simply run the server directly in your terminal:

python server.py

You will see a prompt asking for:

Stock symbol
Timeframe (e.g., 3mo, 1y)

Charts will be displayed in a window, and summaries printed in the terminal.

Example queries:

"Show me Tesla's stock performance over the last 3 months"
"Compare Apple and Microsoft stocks for the past year"
"Analyze the trading volume of Amazon stock for the last month"

The agent will fetch data, run analysis, and render tables or charts inline.

Project Structure Overview

project_root/ ├── server.py # Main entry point for the agent ├── requirements.txt # Optional, or use uv's pyproject.toml └── README.md # You are reading this!

** Tips**

Troubleshooting Ollama: Make sure the Ollama daemon is running (ollama serve) before you start the server.
Customizing the prompt: You can easily edit server.py to adjust the prompt format or add more data processing.