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Arcanna MCP Server: Unmatched Performance, Seamless Scalability - MCP Implementation

Arcanna MCP Server: Unmatched Performance, Seamless Scalability

Arcanna MCP Server: Unleash cutting-edge performance and seamless scalability for your mission-critical operations, redefining enterprise innovation.

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Ranked in the top 4% of all AI tools in its category

About Arcanna MCP Server

What is Arcanna MCP Server: Unmatched Performance, Seamless Scalability?

Arcanna MCP Server is a high-performance middleware enabling users to integrate with Arcanna’s AI models via the Model Context Protocol (MCP). It provides enterprise-grade scalability and reliability for deploying decision-making workflows, event processing, and iterative model training. Key strengths include real-time job management, feedback-driven accuracy improvements, and robust API status validation.

How to use Arcanna MCP Server: Unmatched Performance, Seamless Scalability?

Option 1: uvx Command-Line Setup

  • Install uv CLI
  • Edit your MCP client config file (e.g., claude_desktop_config.json) with this block:
{
    "mcpServers": {
        "arcanna": {
            "command": "uvx",
            "args": ["arcanna-mcp-server"],
            "env": {
                "ARCANNA_INPUT_API_KEY": "your_input_api_key",
                "ARCANNA_MANAGEMENT_API_KEY": "your_management_api_key",
                "ARCANNA_HOST": "your_host_url",
                "ARCANNA_USER": "your_username"
            }
        }
    }}

Option 2: Docker Deployment

  • Install Docker
  • Build the container: docker build -t arcanna-mcp .
  • Configure Docker run command in your MCP client:
{
    "mcpServers": {
        "arcanna-docker": {
            "command": "docker",
            "args": ["run", "-i", "--rm", "-e", "ARCANNA_INPUT_API_KEY", "..."],
            "env": {
                // fill with your credentials
            }
        }
    }}

Arcanna MCP Server Features

Key Features of Arcanna MCP Server: Unmatched Performance, Seamless Scalability?

  • Job Orchestration: Full lifecycle control over AI workflows including start/stop/train operations
  • Event Streaming: Real-time ingestion and decision-making at scale
  • Feedback Loop: Direct integration for user feedback to refine model predictions
  • Health Monitoring: Proactive API key validation and service status checks

Use cases of Arcanna MCP Server: Unmatched Performance, Seamless Scalability?

Typical implementations include:

  • Real-time fraud detection systems with instant model updates
  • Large-scale recommendation engines with dynamic feedback ingestion
  • Production ML pipelines requiring automated retraining workflows
  • High-traffic event processing in IoT or financial trading platforms

Arcanna MCP Server FAQ

FAQ from Arcanna MCP Server: Unmatched Performance, Seamless Scalability?

  • Q: How do I troubleshoot API key errors?
    A: Run health_check endpoint and verify env variables in your config
  • Q: Can I use this with non-MCP clients?
    A: No, MCP protocol compliance is mandatory for server integration
  • Q: What happens during server scaling?
    A: Automatic load balancing maintains consistent API response times
  • Q: How frequent should I send feedback?
    A: Best practice: continuous streaming with batched updates every 15 minutes

Content

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

Option 1 - uvx

Prerequisites

Configuration

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

{
  "mcpServers": {
    "arcanna": {
      "command": "uvx",
      "args": [
        "arcanna-mcp-server"
      ],
      "env": {
        "ARCANNA_INPUT_API_KEY": "YOUR_ARCANNA_INPUT_API_KEY",
        "ARCANNA_MANAGEMENT_API_KEY": "YOUR_ARCANNA_MANAGEMENT_API_KEY",
        "ARCANNA_HOST": "YOUR_ARCANNA_HOST",
        "ARCANNA_USER": "YOUR_USERNAME"
      }
    }
  }
}

Option 2 - 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 config.
{
  "mcpServers": {
    "arcanna-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "ARCANNA_INPUT_API_KEY",
        "-e",
        "ARCANNA_MANAGEMENT_API_KEY",
        "-e",
        "ARCANNA_HOST",
        "-e",
        "ARCANNA_USER",
        "mcp/arcanna"
      ],
      "env": {
        "ARCANNA_INPUT_API_KEY": "<YOUR_ARCANNA_API_KEY_HERE>",
        "ARCANNA_MANAGEMENT_API_KEY": "<ARCANNA_MANAGEMENT_API_KEY>",
        "ARCANNA_HOST": "<YOUR_ARCANNA_HOST_HERE>",
        "ARCANNA_USER": "<YOUR_USERNAME_HERE>"
      }
    }
  }
}

Features

  • Job Management : Create, retrieve, start, stop, and train jobs
  • Event Processing : Send events for AI-powered decision making
  • Feedback System : Provide feedback on decisions to improve model accuracy
  • Health Monitoring : Check server and API key status

Tools

Job Management

  • get_jobs

    • Retrieve all jobs associated with your API key
    • Returns a list of job details including status, labels, and processing metrics

  • get_job_by_id

    • Retrieve specific job details by ID

  • get_job_by_name

    • Retrieve specific job details by name

  • get_job_labels

    • Retrieve decision labels for a specific job

  • get_labels_of_job_by_name

    • Retrieve decision labels for a job by name

  • 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

Event Management

  • send_event

    • Submit an event to Arcanna for AI decision-making

  • send_event_with_id

    • Submit an event with a custom identifier

  • get_event_by_id

    • Retrieve event details and decision results

Feedback System

  • send_feedback_for_event
    • Provide feedback on AI decisions for model improvement

System Health

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

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