LaunchDarkly MCP Server

LaunchDarkly’s Model Context Protocol (MCP) server bridges the gap between feature‑flag management and AI‑powered development workflows. By exposing LaunchDarkly’s rich flag evaluation API as an MCP server, developers can let AI assistants query real‑time feature states, toggle flags on the fly, and retrieve flag metadata—all within the same conversational context that powers coding help or documentation generation. This eliminates the need for developers to write custom integration code, allowing AI tools to treat feature flags as first‑class data sources.

The server acts as a lightweight proxy that translates MCP calls into LaunchDarkly SDK requests. When an AI assistant invokes the method on the resource, for example, the server authenticates with LaunchDarkly using a provided API key, evaluates the requested flag for a given user or context, and returns the value along with any associated metadata. Because the MCP specification guarantees a consistent request/response shape, AI clients can reason about feature flag outcomes without worrying about SDK quirks or authentication flows. This is especially valuable in continuous integration pipelines, where AI tools can automatically adjust feature toggles to test different scenarios or verify deployment readiness.

Key capabilities include:

• Real‑time flag evaluation for any LaunchDarkly feature, supporting user and context targeting.
• Metadata retrieval, such as flag descriptions or variation details, enabling AI assistants to generate documentation or help text that reflects the current flag state.
• Environment awareness, allowing developers to query flags across multiple LaunchDarkly environments (staging, production, etc.) from a single MCP endpoint.
• Secure API key handling, ensuring that sensitive credentials are never exposed in client code.

Typical use cases involve AI‑driven devops workflows: an assistant can suggest which feature flags to enable for a new branch, automatically create or update flag values during a build, or provide status reports on flag rollout progress. In a customer support scenario, the AI can explain why a user is experiencing a particular feature behavior by referencing the flag configuration that governs it. For quality assurance, test suites can query the MCP server to assert expected feature states before and after code changes.

Integrating LaunchDarkly’s MCP server into an AI workflow is straightforward—once the server is running, any MCP‑compatible client (Claude Desktop, Qodo Gen, or a custom cursor configuration) can add the server definition and start issuing calls. The result is a seamless, declarative interface that lets developers and AI assistants share a single source of truth for feature flag logic. By unifying feature management with conversational tooling, LaunchDarkly’s MCP server offers a unique advantage: instant, programmatic access to production‑ready flag data without leaving the AI’s context.