Configure Claude with MCP

Connect Claude Desktop to the Kloudfuse MCP server to investigate infrastructure, query metrics, analyze traces, and troubleshoot services using natural language through the KF Assistant prompt templates.

MCP Prompts

The Model Context Protocol (MCP) provides a standardized method for servers to expose prompt templates to clients. These templates allow the MCP servers to provide structured messages and instructions for interacting with language models. The Kloudfuse MCP Server includes predefined MCP prompt templates. These templates have been tuned to improve query accuracy.

Note: If you have multiple Kloudfuse clusters, create multiple prompts to target each cluster. Otherwise, it might pull data from environments unrelated to the one you are investigating.

KF Assistant

To take advantage of the MCP Prompts, you need to use the kf_assistant.

The KF Assistant prompt provides:

• Consistent Workflow - Guides AI agents through a structured troubleshooting workflow

• Standardized Label usage - Uses existing labels to avoid LLM hallucinations and common mistakes

• Data correlation - The agent will help correlate findings across the various data streams

Customization

To help the KF Assistant, you can disable items that you don’t want it to investigate.

1. Open the Claude AI Desktop

2. Click on the Prompt Button, and click the kf-mcp-${ALIAS} that you want to customize

3. From the Menu that appears you can disable/enable items to focus searches and improve performance. (By default, everything is enabled)

Usage

To use the KF Assistant you will need to select a specific assistant. In particular, you want to do this when you have multiple Assistants set up.

1. Open the Claude AI Desktop

2. Click on the Template Button

3. Click the kf-${KLOUDFUSE_SERVER} assistant you want to use

4. You will now see a prompt for that KF Assistant

These are example natural language queries that you can use with the KF Assistant:

Troubleshooting:

• "Show me error logs for the payment service in the last hour"
• "What traces have errors in the frontend service?"
• "Which pods are crashing in the production namespace?"

Metrics Analysis:

• "Query CPU usage for the payment service"
• "What are the available metrics for the database service?"
• "Show me memory utilization trends over the last 24 hours"

Infrastructure:

• "List all pods in the production namespace"
• "Show me the deployment configuration for the api service"
• "What nodes are running in the cluster?"

Alerts:

• "Show me all firing alerts"
• "What alerts fired in the last 24 hours?"
• "What is the query expression for the high-latency alert?"

Dependencies:

• "What services does the frontend call?"
• "Show me the service dependency graph"
• "Which services depend on the database?"

Best Practices

When troubleshooting, these are some suggested approaches,

• Provide environment details: Include details (service names, namespaces, clusters) about the environment you are investigating.
• Use labels: Using labels will help limit searches to items you are researching. This will improve performance and give more accurate results.
• Specify time ranges: Using specific time ranges will improve the response time and will narrow searches to issues that you are researching at the moment.