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Arcanna MCP Server

Arcanna MCP Server

Local
github.com/siscaleSecurity0Updated today

About Arcanna MCP Server

Arcanna MCP Server connects MCP clients to Arcanna AI use cases. It supports operations across resources, jobs, events, generated code, and feedback, along with server health checks.

Key features

  1. 01Create, update, retrieve, and delete resources
  2. 02Manage and train jobs
  3. 03Query processed events with filters
  4. 04Generate, execute, and save Python code
  5. 05Submit feedback on AI decisions
  6. 06Check server and API key status

Use cases

  1. 01Manage Arcanna jobs and integrations
  2. 02Inspect events processed by Arcanna
  3. 03Build and save code integrations
  4. 04Submit decision feedback for model improvement

MCP support

Exposes tools for Arcanna resource, job, event, code, feedback, and health operations to MCP clients. The README describes configuration for Claude Desktop and other MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Arcanna MCP Server

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

Usage with Claude Desktop or other MCP Clients

Configuration

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

Use docker image (https://hub.docker.com/r/arcanna/arcanna-mcp-server) or PyPi package (https://pypi.org/project/arcanna-mcp-server/)
Building local image from this repository
Prerequisites
Configuration
  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>"
      }
    }
  }
}

Features

  • 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

Tools

Query Arcanna events
  • 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)
Resource Management
  • 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
Python Coding
  • 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
Job Management
  • 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
Feedback System
  • add_feedback_to_event
    • Provide feedback on AI decisions for model improvement
System Health
  • health_check
    • Verify server status and Management API key validity
    • Returns Management API key authorization status

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.