MCP Servers

KappaML MCP Server

Exposes the KappaML API as tools for AI assistants, enabling user management, model operations, predictions, learning, forecasting, metrics, and checkpoint management.

README

KappaML MCP Server

Remote MCP server that exposes the KappaML API as tools for AI assistants like Claude Desktop and Claude Code.

Tools (21)

Category	Tools
Users	`get_user_profile`, `update_user_profile`
API Keys	`create_api_key`, `list_api_keys`, `delete_api_key`
Models	`list_models`, `create_model`, `get_model`, `delete_model`
Predict	`predict`, `predict_batch`
Learn	`learn`, `learn_batch`
Forecast	`forecast`
Metrics	`get_metrics`, `get_metrics_history`
Checkpoints	`list_checkpoints`, `create_checkpoint`, `get_checkpoint`, `delete_checkpoint`, `restore_checkpoint`

Setup

Install locally

cd kappaml-mcp
pip install -e .

Run the server

# Option 1: Set API key via environment variable (single-tenant)
export KAPPAML_API_KEY=sk.xxx
python server.py

# Option 2: Clients send their own key via Authorization header (multi-tenant)
python server.py

The server starts on http://0.0.0.0:8000.

Docker

docker build -t kappaml-mcp .
docker run -p 8000:8000 -e KAPPAML_API_KEY=sk.xxx kappaml-mcp

Client Configuration

Claude Code

claude mcp add --transport http kappaml http://localhost:8000/mcp \
  --header "Authorization: Bearer sk.xxx"

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "kappaml": {
      "url": "http://localhost:8000/mcp",
      "headers": {
        "Authorization": "Bearer sk.xxx"
      }
    }
  }
}

Authentication

The server extracts the API key from the Authorization: Bearer <key> header sent by the MCP client and forwards it as X-API-Key to the KappaML API. If no header is present, it falls back to the KAPPAML_API_KEY environment variable.

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