What is Akshare Mcp Server

The akshare_mcp_server is a Model Context Protocol (MCP) server that leverages the AKShare library to provide financial data analysis capabilities, enabling users to access a wide range of financial market data.

Use cases

Use cases for akshare_mcp_server include financial data analysis, investment research, market trend analysis, and integration with other applications for enhanced financial insights.

How to use

To use the akshare_mcp_server, clone the repository, set up a virtual environment, install dependencies, and run the server. Integration with Claude Desktop requires specific configuration in its settings.

Key features

Key features include access to Chinese and global financial market data, integration with Claude Desktop via MCP protocol, and support for various financial data queries such as stocks, funds, bonds, futures, forex, and macroeconomic data.

Where to use

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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:

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 in src/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 in src/mcp_server_akshare/server.py:

   class AKShareTools(str, Enum):
       # Existing tools...
       NEW_TOOL_NAME = "new_tool_name"
   

3. Import the new function in src/mcp_server_akshare/server.py:

   from .api import (
       # Existing imports...
       fetch_new_data_function,
   )
   

4. Add the tool definition to the handle_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 the handle_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