LaunchDarkly MCP Server

What does “LaunchDarkly” MCP Server do?

The LaunchDarkly MCP (Model Context Protocol) Server is an official implementation that connects AI assistants and agents with LaunchDarkly’s feature management platform via the Model Context Protocol. This server acts as a bridge, enabling AI tools to interact programmatically with LaunchDarkly’s external data sources, APIs, and services. By integrating with the LaunchDarkly MCP Server, developers and AI systems can perform automated tasks such as querying feature flag statuses, managing environments, and orchestrating feature rollouts. This enhances development workflows by facilitating seamless access to LaunchDarkly’s capabilities directly from AI-powered tools, allowing for streamlined collaboration, rapid experimentation, and improved deployment safety.

List of Prompts

No prompt templates were mentioned in the available documentation or repository files.

List of Resources

No explicit resources were listed in the available documentation or repository files.

List of Tools

No specific tools were enumerated in the available documentation or repository files, including the server implementation.

Use Cases of this MCP Server

• Feature Flag Management
  AI assistants can interact with LaunchDarkly’s API to automate the creation, modification, and status checks of feature flags, improving efficiency and reducing manual errors.
• Environment Configuration
  Developers can use the MCP server to switch, manage, or audit different environments through AI queries, simplifying environment management tasks.
• Automated Rollouts and Experimentation
  The server enables orchestrating feature rollouts and experiments, allowing AI agents to analyze results and make recommendations or changes programmatically.
• Monitoring and Compliance
  Integrate with monitoring tools to ensure feature flag usage adheres to compliance requirements, with AI agents proactively surfacing configuration or usage issues.
• Collaboration and Workflow Automation
  Teams can automate repetitive LaunchDarkly tasks directly from their AI clients, supporting faster iterations and reducing context switching.

How to set it up

Windsurf

No Windsurf-specific setup instructions found in the documentation.

Claude

1. Obtain your LaunchDarkly API key from the LaunchDarkly Authorization page.
2. Open your claude_desktop_config.json file.
3. Add the following to your mcpServers object:

   {
     "mcpServers": {
       "LaunchDarkly": {
         "command": "npx",
         "args": [
           "-y", "--package", "@launchdarkly/mcp-server", "--", "mcp", "start",
           "--api-key", "api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
         ]
       }
     }
   }
   

4. Save the file.
5. Restart Claude and verify the MCP server is connected.

Securing API Keys:
Use environment variables for sensitive data:

{
  "mcpServers": {
    "LaunchDarkly": {
      "env": {
        "LD_API_KEY": "api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
      },
      "inputs": {
        "api-key": "${LD_API_KEY}"
      }
    }
  }
}

Cursor

1. Obtain your LaunchDarkly API key.
2. Create a .cursor/mcp.json file in your project root.
3. Add the following:

   {
     "mcpServers": {
       "LaunchDarkly": {
         "command": "npx",
         "args": [
           "-y", "--package", "@launchdarkly/mcp-server", "--", "mcp", "start",
           "--api-key", "api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
         ]
       }
     }
   }
   

4. Save the file.
5. Restart Cursor and verify the MCP server is connected.

Securing API Keys:
Use environment variables as above.

Cline

No Cline-specific setup instructions found in the documentation.

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:

{
  "LaunchDarkly": {
    "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 “LaunchDarkly” to whatever the actual name of your MCP server instance is and replace the URL with your own MCP server URL.

Overview

Based on the above, the LaunchDarkly MCP Server provides a solid overview and setup instructions but lacks documentation or examples for prompts, resources, and tools. Thus, while it is easy to install, it is currently less developer-friendly for advanced MCP use cases.

MCP Score

Score:
Based on the documentation, setup clarity, and presence of a license, but lack of resource/tool/prompt details, I would rate this MCP server a 4/10 for out-of-the-box developer experience and advanced MCP features.