AKShare MCP Server: Real-Time Analytics & Seamless Scalability

AKShare MCP Server

A Model Context Protocol (MCP) server that provides financial data analysis capabilities using the AKShare library.

Features

• Access to Chinese and global financial market data through AKShare
• Integration with Claude Desktop via MCP protocol
• Support for various financial data queries and analysis

Installation

Using uv (recommended)

# Clone the repository
git clone https://github.com/yourusername/akshare_mcp_server.git
cd akshare_mcp_server

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies with uv
uv pip install -e .

Using pip

# Clone the repository
git clone https://github.com/yourusername/akshare_mcp_server.git
cd akshare_mcp_server

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

Usage

Running the server

# Activate the virtual environment
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Run the server
python run_server.py

Integrating with Claude Desktop

1. Add the following configuration to your Claude Desktop configuration:

"mcpServers": {
    "akshare-mcp": {
        "command": "uv",
        "args": [
            "--directory",
            "/path/to/akshare_mcp_server",
            "run",
            "akshare-mcp"
        ],
        "env": {
            "AKSHARE_API_KEY": "<your_api_key_if_needed>"
        }
    }
}

2. Restart Claude Desktop
3. Select the AKShare MCP server from the available tools

Available Tools

The AKShare MCP server provides the following tools:

• Stock data queries
• Fund data queries
• Bond data queries
• Futures data queries
• Forex data queries
• Macroeconomic data queries
• And more...

Adding a New Tool

To add a new tool to the MCP server, follow these steps:

1. Add a new API function insrc/mcp_server_akshare/api.py:

   async def fetch_new_data_function(param1: str, param2: str = "default") -> List[Dict[str, Any]]:
   """
   Fetch new data type.

   Args:
   param1: Description of param1
   param2: Description of param2
   """
   try:
   df = ak.akshare_function_name(param1=param1, param2=param2)
   return dataframe_to_dict(df)
   except Exception as e:
   logger.error(f"Error fetching new data: {e}")
   raise

2. Add the new tool to the enum insrc/mcp_server_akshare/server.py:

   class AKShareTools(str, Enum):

   Existing tools...

   NEW_TOOL_NAME = "new_tool_name"

3. Import the new function insrc/mcp_server_akshare/server.py:

   from .api import (

   Existing imports...

   fetch_new_data_function,

)

4. Add the tool definition to thehandle_list_tools() function:

   types.Tool(
   name=AKShareTools.NEW_TOOL_NAME.value,
   description="Description of the new tool",
   inputSchema={
   "type": "object",
   "properties": {
   "param1": {"type": "string", "description": "Description of param1"},
   "param2": {"type": "string", "description": "Description of param2"},
   },
   "required": ["param1"], # List required parameters
   },

),

5. Add the tool handler in thehandle_call_tool() function:

   case AKShareTools.NEW_TOOL_NAME.value:
   param1 = arguments.get("param1")
   if not param1:
   raise ValueError("Missing required argument: param1")
   param2 = arguments.get("param2", "default")

   result = await fetch_new_data_function(
   param1=param1,
   param2=param2,
   )

6. Test the new tool by running the server and making a request to the new tool.

Development

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

Docker

You can also run the server using Docker:

# Build the Docker image
docker build -t akshare-mcp-server .

# Run the Docker container
docker run -p 8000:8000 akshare-mcp-server

License

MIT