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LaunchDarkly's Model Context Protocol (MCP) Server

LaunchDarkly's Model Context Protocol (MCP) Server

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github.com/launchdarklyDeveloper Tools27Updated 1d ago

About LaunchDarkly's Model Context Protocol (MCP) Server

A self-managed MCP server that connects AI clients to LaunchDarkly, intended for EU and Federal instances that do not support its hosted server. It exposes operations for feature flags, AI Configs, experiments, metrics, environments, audit logs, and repositories.

Key features

  1. 01Manage feature flags and targeting
  2. 02Create and update AI Configs
  3. 03Create and inspect experiments
  4. 04Manage metrics and environments
  5. 05Read audit logs and repositories

Use cases

  1. 01Check flag status across environments
  2. 02Adjust AI Config targeting
  3. 03Review experiment and audit data

MCP support

Exposes LaunchDarkly resources and operations to MCP-compatible AI clients. It supports local installation and Docker deployment, with API-key authentication.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"launchdarkly": {
"command": "npx",
"args": [
"-y",
"@launchdarkly/mcp-server"
]
}
}
}
Run commandnpx -y @launchdarkly/mcp-server

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@launchdarkly/mcp-server

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

LaunchDarkly local MCP server

The local Model Context Protocol (MCP) server for LaunchDarkly federal and European Union (EU) environments.

Important

Use the LaunchDarkly hosted MCP server where available

LaunchDarkly strongly recommends using the hosted MCP server where it is available. The hosted server is more feature complete and receives more frequent updates than this self-managed server.

LaunchDarkly provides this self-managed MCP server for LaunchDarkly EU or Federal instances, which do not yet support the hosted MCP.

Table of Contents

Installation

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.

Runtime requirements

Important

Some eMudhra intermediate certificate authorities were compromised in October 2026 (advisory). Anyone on your network who holds one of their keys could intercept the server's connection to LaunchDarkly and read your API access token.

  • npx and local installs: use Node.js 22.23.4, 24.22.1, 26.12.1 or later. These releases reject the compromised intermediates. The server also rejects them itself and prints a warning on startup when your runtime does not.
  • Docker: use image 0.7.1 or later. Those run on Node.js 24.22.1; 0.7.0 and earlier run on Node.js 24.2.0.
  • Standalone binaries: stop using the mcp-server-bun-* binaries attached to releases v0.6.1 and earlier. They embed a Bun runtime that does not reject the compromised intermediates, and they are no longer published. Switch to npx with a patched Node.js or to Docker.
Cursor installation steps

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.

Claude installation steps

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.

Qodo Gen installation steps
  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.

  1. Click Save.
GitHub Copilot CLI installation steps

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.

Installation steps from a local clone

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"
      ]
    }
  }
}
Installation steps using environment variables

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"
      }
    }
  }
}
Installation steps for Docker

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 127.0.0.1:8080:8080 709825985650.dkr.ecr.us-east-1.amazonaws.com/launchdarkly/mcp --api-key api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --auth-token "$MCP_AUTH_TOKEN"

The container listens on 0.0.0.0, so --auth-token (at least 16 characters) is required. Clients must send Authorization: Bearer <token> on /sse and /message. Outside Docker, the SSE transport listens on 127.0.0.1 by default; pass --host to change it, which also requires --auth-token for non-loopback hosts.

Then, configure your server definition to point at the running container. For example:

{
  "mcpServers": {
    "launchdarkly": {
      "url": "http://localhost:8080/sse",
      "headers": {
        "Authorization": "Bearer <your --auth-token value>"
      }
    }
  }
}

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.

Requirements

For supported JavaScript runtimes, please consult RUNTIMES.md.

Available Resources and Operations

Available methods
AiConfigs
AuditLog
CodeReferences
Environments
Experiments
FeatureFlags
  • 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
Metrics

Available Environments

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.

EnvironmentServer URL
Commercial (Default)https://app.launchdarkly.com
Federalhttps://app.launchdarkly.us
EUhttps://app.eu.launchdarkly.com

Contributions

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.

About LaunchDarkly

  • 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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.