VictoriaMetrics MCP Server

What does “VictoriaMetrics” MCP Server do?

The VictoriaMetrics MCP Server is an implementation of the Model Context Protocol (MCP) designed to connect AI assistants with the VictoriaMetrics time series database. This server acts as a middleware, allowing AI agents and development tools to interact with VictoriaMetrics through standardized MCP interfaces. By bridging AI clients and VictoriaMetrics, it enables enhanced development workflows such as querying metrics, managing time series data, and integrating monitoring insights directly into AI-driven processes. This connectivity streamlines tasks like database queries, real-time data analysis, and automation of metric retrieval, providing developers with a powerful tool to incorporate external data into their LLM applications and workflows.

List of Prompts

No prompt templates are documented or mentioned in the available repository content.

List of Resources

No explicit resources are documented or listed in the available repository content.

List of Tools

No tools are directly listed or described in the available repository content or server files.

Use Cases of this MCP Server

• Database Management: Enables AI agents to interact with the VictoriaMetrics database for querying and managing time series data.
• Monitoring Integration: Allows integration of real-time metrics from VictoriaMetrics into intelligent assistants or workflows.
• Time Series Analysis: Supports AI-driven analysis and interpretation of time series data, useful for anomaly detection and trend analysis.
• Automation of Metric Retrieval: Facilitates the automation of fetching relevant metrics and insights for applications, dashboards, or alerting systems.
• Contextual Data Augmentation: Enhances LLMs and agents by providing contextual monitoring data directly from VictoriaMetrics.

How to set it up

Windsurf

1. Ensure prerequisites such as Node.js are installed.
2. Locate your Windsurf configuration file.
3. Add the VictoriaMetrics MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "victoriametrics": {
         "command": "npx",
         "args": ["@victoriametrics/mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the server status.

Securing API Keys

Use environment variables to secure API keys:

{
  "mcpServers": {
    "victoriametrics": {
      "command": "npx",
      "args": ["@victoriametrics/mcp-server@latest"],
      "env": {
        "VICTORIAMETRICS_API_KEY": "${VICTORIAMETRICS_API_KEY}"
      },
      "inputs": {
        "api_key": "${VICTORIAMETRICS_API_KEY}"
      }
    }
  }
}

Claude

1. Install necessary prerequisites.
2. Open the configuration file for Claude.
3. Add the following configuration:

   {
     "mcpServers": {
       "victoriametrics": {
         "command": "npx",
         "args": ["@victoriametrics/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart the Claude service.
5. Confirm connectivity to the MCP server.

Securing API Keys

Same as above.

Cursor

1. Make sure Node.js and other dependencies are installed.
2. Edit the Cursor configuration file.
3. Insert the MCP server entry:

   {
     "mcpServers": {
       "victoriametrics": {
         "command": "npx",
         "args": ["@victoriametrics/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Validate that the MCP server is running.

Securing API Keys

Same as above.

Cline

1. Prepare your environment (install Node.js, etc.).
2. Open Cline’s configuration.
3. Add the VictoriaMetrics MCP Server block:

   {
     "mcpServers": {
       "victoriametrics": {
         "command": "npx",
         "args": ["@victoriametrics/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Cline.
5. Verify setup via logs or status checks.

Securing API Keys

Same as above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "victoriametrics": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “victoriametrics” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the tables above, the VictoriaMetrics MCP Server provides basic documentation and standard setup instructions but lacks detailed information on prompts, resources, and tools. Its core value lies in its role as a bridge to VictoriaMetrics, but it would benefit from more comprehensive documentation. I would rate this MCP a 4/10 in its current state for completeness and developer-friendliness.

MCP Score