Unleash MCP Server

The Unleash MCP Server bridges the gap between large‑language‑model (LLM) applications and a feature‑flagging system, allowing AI assistants to query, create, update, and list feature flags directly through the Model Context Protocol. By exposing Unleash’s REST API over MCP, developers can treat feature toggles as first‑class resources within their AI workflows. This eliminates the need for separate HTTP clients or manual API calls, enabling a seamless integration where an LLM can ask whether a feature is enabled and receive an immediate, authoritative answer.

At its core, the server implements standard MCP concepts: resources for flags and projects, tools that perform CRUD operations on those resources, and prompts that let an LLM formulate a natural‑language request into a concrete tool invocation. For example, the “get‑flag” tool retrieves a flag’s status and metadata, while the “batch‑flag‑check” prompt allows an assistant to evaluate multiple flags in one call. These abstractions let developers compose complex feature‑flag logic without writing boilerplate code, making it easier to embed conditional behavior directly into conversational agents.

Key capabilities include:

• Real‑time flag status: The server can query Unleash on demand, ensuring the LLM always reflects the latest configuration.
• Flag lifecycle management: Create and update flags programmatically, allowing AI assistants to modify application behavior on the fly (e.g., enabling a beta feature for a specific user segment).
• Project enumeration: List all Unleash projects, giving the LLM context about the scope of available flags.
• MCP‑native transport: Supports both HTTP/SSE and stdio transports, so the server can run in a variety of deployment environments—from local development to cloud functions.

In practice, this MCP server is invaluable for use cases such as dynamic feature rollouts within chatbots, A/B testing of AI responses, or policy‑driven content delivery. A conversational agent can check a flag before suggesting a new feature, or an AI‑powered deployment pipeline can toggle flags automatically after successful tests. By integrating directly into the LLM’s context, developers gain fine‑grained control over feature exposure without leaving the assistant’s conversational flow.

The Unleash MCP Server stands out because it unifies feature‑flag management with the standardized Model Context Protocol, providing a clean, declarative interface for AI developers. It removes the friction of manual API handling, ensures consistent flag state across distributed services, and empowers assistants to adapt behavior in real time based on configuration changes—all while staying within the familiar MCP ecosystem.