Arcanna MCP Server
Arcanna MCP Server is a service that interacts with Arcanna AI use cases through the Model Context Protocol (MCP), providing functions such as resource management, Python coding, event query, task management, feedback system, and health monitoring.
rating : 2 points
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What is Arcanna MCP Server?
Arcanna MCP Server is a middleware service based on the Model Context Protocol (MCP), which acts as a bridge between users and the Arcanna AI platform. Through the standardized MCP protocol, users can conveniently manage AI resources, execute code, query event data, etc.How to use Arcanna MCP Server?
You can connect to Arcanna MCP Server through any MCP protocol-compatible client (such as Claude Desktop). First, you need to configure the server connection information, and then you can use various tools to interact with the Arcanna platform.Applicable scenarios
Suitable for scenarios that require programmatic interaction with the Arcanna AI platform, including automated AI workflow management, batch data processing, and model training feedback collection.Main features
Resource managementCreate, update, and retrieve Arcanna resources (jobs, integrations, etc.)
Python programming supportCode generation, execution, and saving code blocks as Arcanna integrations
Event queryQuery event data processed by Arcanna, supporting multiple filtering conditions
Job managementCreate, retrieve, start, stop, and train jobs
Feedback systemProvide decision feedback to improve model accuracy
Health monitoringCheck server status and API key validity
Advantages and limitations
Advantages
Standardized protocol: Using the MCP protocol ensures compatibility with various clients
Comprehensive functions: Covers the main operation functions of the Arcanna platform
Easy to integrate: Provides two deployment methods, Docker image and PyPi package
Limitations
Requires API key authorization for access
Only supports the Python code execution environment
Depends on the Arcanna platform infrastructure
How to use
Installation and deployment
Choose the Docker image or PyPi package method to deploy the service
Client configuration
Add server connection information to the MCP client configuration file
Connection test
Use the health_check tool to verify whether the connection is successful
Usage examples
Event data analysisQuery high-priority events within a specific time period and analyze trends
Automated workflow creationGenerate and deploy a data processing workflow
Frequently Asked Questions
How to obtain the ARCANNA_MANAGEMENT_API_KEY?
Which MCP clients are supported?
What are the limitations of code execution?
Related resources
Docker image
The official Docker image of Arcanna MCP Server
PyPi package
The Python package of Arcanna MCP Server
MCP protocol documentation
The official documentation of the Model Context Protocol
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