Overview
What is Arcanna Input MCP Server?
Arcanna Input MCP Server enables users to interact with Arcanna's AI use cases that use external API integration through the Model Context Protocol (MCP). It is designed for developers using MCP clients like Claude Desktop.
How to use Arcanna Input MCP Server?
Configure the server in your MCP client configuration file (e.g., claude_desktop_config.json) using a Docker image or PyPI package. Set the environment variables ARCANNA_INPUT_API_KEY, ARCANNA_HOST, and ARCANNA_USER. Then invoke the MCP client as usual to access the exposed tools.
Key features of Arcanna Input MCP Server
- 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
Use cases of Arcanna Input MCP Server
- Automate AI job lifecycle management from an MCP client
- Submit events to Arcanna for real-time decision making
- Retrieve and monitor job status and processing metrics
- Provide feedback to fine-tune AI models
FAQ from Arcanna Input MCP Server
What are the prerequisites to run Arcanna Input MCP Server?
Docker is required if using the Docker image. Alternatively, you can install the server via PyPI.
How do I configure the server for use with Claude Desktop?
Add the entry shown in the README to the mcpServers section of your claude_desktop_config.json, providing your Arcanna API key, host, and username as environment variables.
What tools does Arcanna Input MCP Server provide?
It provides tools for job management (get all jobs, get job by ID, get job by name, get job labels), event management (send event, send event with custom ID), and health checking.
What runtime dependencies does the server have?
The server requires an MCP client to connect, and depending on the install method, a Docker runtime or Python environment. No additional runtime beyond the API key and host are specified.
