stock_intelligence_mcp by zhenxiay - MCP Server

📈📉📊 stock_intelligence_mcp

A Model Context Protocol (MCP) server which allows AI agents to interact with Yahoo Finance.

✨ Key Features

Feature	Description
📈 Stock intelligence toolkit	Get most recent price development and news of a selected ticker.
📊 Recommendation toolkit	Get recommendations based on technical analysis indicators or analysts' opinions.
🔌 Seamless plugin with client	The server can be integrated with the main stream mcp clients (Claude, VS code etc.) seamlessly.
🖥️ CLI Interface	Offers a CLI interface to start the server.
🐳 Docker-Ready	Dockerfile availiable.
📦 Extensible	The server can be extended with addtional tools easily.

🚀 Getting Started

To start this server, you can either clone this repository and run the server with python, or use the Docker image in the repo.

🧑‍💻 Option 1: Run with python (using uv)

Create a new directory for your project

⚙️ Clone the repository:

git clone https://github.com/zhenxiay/stock_intelligence_mcp.git
cd stock_intelligence_mcp

Create virtual environment in the folder and activate it

uv venv
source .venv/bin/activate

🚀 Start the server

The server offers a CLI interface for the start.

Run the following command to check the availiable parameters:

uv run src/main.py --help

An example of starting the server with port 8008, streamable-http as transport and example_server as name:

uv run src/main.py --name example_server --port 8008 --transport streamable-http

🧑‍💻 Option 2: Run with Docker/ k8s/ Helm chart

❗Make sure that Docker engine/ a k8s cluster (minikube/ kind etc.)/ Helm is installed on your PC.

Create a new directory for your project

⚙️ Clone the repository:

git clone https://github.com/zhenxiay/stock_intelligence_mcp.git
cd stock_intelligence_mcp

Build the Docker container

docker build . -t mcp-server-stock-intelligence:test -f Docker/Dockerfile

Run the Docker container

docker run --name mcp-server-stock -p 8008:8008 mcp-server-stock-intelligence:test

Run the k8s manifest

❗A secret has first to be created with some proxy specifications as env variable has to be created:

kubectl create secret generic proxy-env --from-literal=HTTP_PROXY=http://localhost:3128 --from-literal=HTTPS_PROXY=https://localhost:3128 --from-literal=NO_PROXY=localhost,127.0.0.1,kind

Then apply the deployment and service:

kubectl apply -f k8s/mcp-server-deployment.yaml
kubectl apply -f k8s/mcp-server-service.yaml

Run Helm to create the server

You can use the Helm chart and the following commands to create the server as well:

helm upgrade --install mcp-server-stock-intelligence k8s/helm --set proxy.http="http://your-proxy:8080" --set proxy.https="http://your-proxy:8080"

⚙️ Connect to the Server

Here are some options with which you can connect the test this server:

🤖 Use MCP inspector:

Follow the instruction under: https://modelcontextprotocol.io/legacy/tools/inspector

🤖 Use a client (Claude Desktop, VS COde Copilot etc.)

Most of the clients would require you to configure your server in a json format like the following:

Example of using streamable_http transport

{
   "mcpServers": {
       "stock_intelligence": {
           "url": "http://localhost:8008/mcp/",
           "transport": "streamable_http"
       }
   }
}

Example of using sse transport

{
   "mcpServers": {
      "stock_intelligence_sse": {
         "transport": "sse",
         "url": "https://localhost:8008/sse",
         "timeout": 600,
         "headers": null,
         "sse_read_timeout": 900
     }
   }
}

❗Please notice that the real port number is related to your own settings on your py command/ docker container/ helm chart values.

Exact instructions for Claude desktop or VS code can be found here:

📋 Claude Desktop: https://modelcontextprotocol.io/quickstart/server

📋 VS Code GitHub Copilot Extention: https://code.visualstudio.com/docs/copilot/chat/mcp-servers