The Arcanna MCP server allows user to interact with Arcanna's AI use cases through the Model Context Protocol (MCP).

Add the following entry to the mcpServers section in your MCP client config file (claude_desktop_config.json for Claude Desktop).

• Docker - https://docs.docker.com/engine/install/

1. Change directory to the directory where the Dockerfile is.
2. Run docker build -t arcanna/arcanna-mcp-server . --progress=plain --no-cache
3. Add the configuration bellow to your claude desktop/mcp client config.

{
  "mcpServers": {
    "arcanna-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "ARCANNA_MANAGEMENT_API_KEY",
        "-e",
        "ARCANNA_HOST",
        "arcanna/arcanna-mcp-server"
      ],
      "env": {
        "ARCANNA_MANAGEMENT_API_KEY": "<ARCANNA_MANAGEMENT_API_KEY>",
        "ARCANNA_HOST": "<YOUR_ARCANNA_HOST_HERE>"
      }
    }
  }
}

• Resource Management: Create, update and retrieve Arcanna resources (jobs, integrations)
• Python Coding: Code generation, execution and saving the code block as an Arcanna integration
• Query Arcanna events: Query events processed by Arcanna
• Job Management: Create, retrieve, start, stop, and train jobs
• Feedback System: Provide feedback on decisions to improve model accuracy
• Health Monitoring: Check server and API key status

• query_arcanna_events

  • Used to get events processed by Arcanna, multiple filters can be provided

• get_filter_fields

  • used as a helper tool (retrieve Arcanna possible fields to apply filters on)

• upsert_resources

  • Create/update Arcanna resources

• get_resources

  • Retrieve Arcanna resources (jobs/integrations)

• delete_resources

  • Delete Arcanna resources

• integration_parameters_schema

  • used in this context as a helper tool

• generate_code_agent

  • Used to generate code

• execute_code

  • Used to execute the generated code

• save_code

  • Use to save the code block in Arcanna pipeline as an integration

• start_job

  • Begin event ingestion for a job

• stop_job

  • Stop event ingestion for a job

• train_job

  • Train the job's AI model using the provided feedback

• add_feedback_to_event
  • Provide feedback on AI decisions for model improvement

• health_check
  • Verify server status and Management API key validity
  • Returns Management API key authorization status