Angel One MCP (Model Context Protocol)
@ankushCodeX
About Angel One MCP (Model Context Protocol)
Local MCP server connecting to Angle One SDK
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcpserver-AngleOne": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
No tools detected
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Overview
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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