The official Model Context Protocol (MCP) server for LaunchDarkly.

• LaunchDarkly's Model Context Protocol (MCP) Server
  • Installation
  • Requirements
  • Available Resources and Operations
  • Available Environments
  • Contributions
  • About LaunchDarkly

This MCP server can be installed in any AI client that supports the MCP protocol. Refer to your AI client's instructions if it isn't listed here.

Create a .cursor/mcp.json file in your project root with the following content:

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

Specify your API key as found on LaunchDarkly's Authorization page.

Add the following server definition to your claude_desktop_config.json file:

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

Specify your API key as found on LaunchDarkly's Authorization page.

1. Open Qodo Gen chat panel in VSCode or IntelliJ.
2. Click Connect more tools.
3. Click + Add new MCP.
4. Add the following configuration:

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

Specify your API key as found on LaunchDarkly's Authorization page.

5. Click Save.

Use the Copilot CLI to interactively add the MCP server:

/mcp add

Or create/edit ~/.copilot/mcp-config.json with the following content:

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

Specify your API key as found on LaunchDarkly's Authorization page.

For more information, see the GitHub Copilot CLI documentation.

You can also run the MCP server as a standalone binary with no additional dependencies. You must pull these binaries from available GitHub releases while specifying the appropriate tag value:

curl -L -o mcp-server https://github.com/launchdarkly/mcp-server/releases/download/{tag}/mcp-server-bun-darwin-arm64 && \
chmod +x mcp-server

You can also run the MCP server locally by cloning this repository. Once cloned, you'll need to install dependencies (npm install) and build the server (npm run build).

Then, configure your server definition to reference your local clone. For example:

{
  "mcpServers": {
    "launchdarkly": {
      "command": "node",
      "args": [
        "/path/to/mcp-server/bin/mcp-server.js", "start",
        "--api-key", "api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
      ]
    }
  }
}

Some AI clients allow for accessing environment variables within MCP configurations. In AI clients where this is supported, you can configure your MCP server to read from an environment variable like MCP_LD_TOKEN as follows:

{
  "mcpServers": {
    "launchdarkly": {
      "command": "npx",
      "args": [
        "-y", "--package", "@launchdarkly/mcp-server", "--", "mcp", "start",
        "--api-key", "$LD_ACCESS_TOKEN"
      ],
      "env": {
        "LD_ACCESS_TOKEN": "MCP_LD_TOKEN"
      }
    }
  }
}

If installing the MCP server from the AWS Markeplace, pull the image using an authenticated role using the provided instructions in the marketplace listing.

To run the container:

docker run --rm -p 8080:8080 709825985650.dkr.ecr.us-east-1.amazonaws.com/launchdarkly/mcp --api-key api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx 

Then, configure your server definition to reference your local clone. For example:

{
  "mcpServers": {
    "launchdarkly": {
      "command": "npx",
      "args": [
        "-y", "--package", "@launchdarkly/mcp-server", "--", "mcp", "start",
        "--api-key", "$LD_ACCESS_TOKEN"
      ],
      "env": {
        "LD_ACCESS_TOKEN": "MCP_LD_TOKEN"
      }
      "url": "http://localhost:8080/sse"
    }
  }
}

This won't work in AI clients (such as Cursor) which don't support accessing environment variables directly within MCP configurations. As a workaround, you can invoke a local script from your MCP configuration. See here for an example.

For supported JavaScript runtimes, please consult RUNTIMES.md.

• getTargeting - Show an AI Config's targeting
• updateTargeting - Update AI Config targeting
• list - List AI Configs
• create - Create new AI Config
• delete - Delete AI Config
• get - Get AI Config
• update - Update AI Config
• createVariation - Create AI Config variation
• deleteVariation - Delete AI Config variation
• getVariation - Get AI Config variation
• updateVariation - Update AI Config variation

• listRepositories - List repositories

• listByProject - List environments

• getStatus - Get flag status across environments
• list - List feature flags
• create - Create a feature flag
• get - Get feature flag
• patch - Update feature flag
• delete - Delete feature flag

Most customer accounts run on LaunchDarkly's commercial (default) environment. Customers on other environments can specify the --server-url argument to connect to the appropriate environment. For example, customers on LaunchDarkly's Federal environment should specify the --server-url https://app.launchdarkly.us argument when starting their MCP server.

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

• LaunchDarkly is a continuous delivery platform that provides feature flags as a service and allows developers to iterate quickly and safely. We allow you to easily flag your features and manage them from the LaunchDarkly dashboard. With LaunchDarkly, you can:
  • Roll out a new feature to a subset of your users (like a group of users who opt-in to a beta tester group), gathering feedback and bug reports from real-world use cases.
  • Gradually roll out a feature to an increasing percentage of users, and track the effect that the feature has on key metrics (for instance, how likely is a user to complete a purchase if they have feature A versus feature B?).
  • Turn off a feature that you realize is causing performance problems in production, without needing to re-deploy, or even restart the application with a changed configuration file.
  • Grant access to certain features based on user attributes, like payment plan (eg: users on the ‘gold’ plan get access to more features than users in the ‘silver’ plan).
  • Disable parts of your application to facilitate maintenance, without taking everything offline.
• LaunchDarkly provides feature flag SDKs for a wide variety of languages and technologies. Read our documentation for a complete list.
• Explore LaunchDarkly
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  • launchdarkly.com for more information
  • launchdarkly.com/docs for our documentation and SDK reference guides
  • launchdarkly.com/docs/api for our API documentation
  • blog.launchdarkly.com for the latest product updates