Angel One MCP (Model Context Protocol)
@ankushCodeX
关于 Angel One MCP (Model Context Protocol)
Local MCP server connecting to Angle One SDK
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcpserver-AngleOne": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is Angel One MCP?
Angel One MCP is a Model Context Protocol server that enables interaction with Angel One’s trading APIs to retrieve historical data and portfolio data. It is designed for developers who use MCP-compatible clients (e.g., Anthropic Desktop, Cline) and have an Angel One trading account.
How to use Angel One MCP?
Clone the repository and install dependencies via uv pip install -r requirements.txt. Hardcode your Angel One API credentials (API key, client code, password, token) in api.py, then start the server by configuring your MCP client (e.g., Anthropic Desktop or Cline) to run api.py using uv.
Key features of Angel One MCP
- Fetch historical trading data from Angel One.
- Retrieve current portfolio information.
- Integrates with MCP-compatible AI clients.
- Configured via a single
api.pyfile. - Uses the
uvpackage manager for dependency management.
Use cases of Angel One MCP
- Query past stock price history for analysis.
- Check real-time portfolio holdings and positions.
- Automate trading data retrieval within AI-powered workflows.
FAQ from Angel One MCP
What are the prerequisites for Angel One MCP?
You need Python 3.10, an active Angel One trading account, and API credentials (API key, client code, password, and TOTP token).
How do I configure the credentials in Angel One MCP?
Edit api.py and hardcode your API_KEY, CLIENT_CODE, PASSWORD, and TOKEN (obtained from the Angel One TOTP enablement page).
How do I install Angel One MCP dependencies?
Clone the repository, create a virtual environment, install uv using the provided one-liner for your OS, then run uv pip install -r requirements.txt.
How do I start Angel One MCP as a server?
Update your MCP client’s settings (e.g., cloud_desktop_config.json or cline_mcp_settings.json) to run C:/Users/<user>/.local/bin/uv --directory <project-path> run api.py.
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