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Unleash MCP Server

A purpose-driven Model Context Protocol (MCP) server for managing Unleash feature flags. This server enables LLM-powered coding assistants to create and manage feature flags following Unleash best practices.

To share feedback, join our community Slack or open an issue on GitHub.

Overview

This MCP server provides tools that integrate with the Unleash Admin API, allowing AI coding assistants to:

• Create feature flags with proper validation and typing.
• Detect existing flags to prevent duplicates or encourage reuse.
• Evaluate changes to decide when a feature flag is needed.
• Stream progress for visibility during operations.
• Handle errors gracefully with helpful hints.
• Report problems with the MCP to Unleash through send_feedback, only after the user opts in.
• Follow best practices from the Unleash documentation.

Available tools

The MCP server exposes the following tools:

• create_flag: Creates a feature flag in Unleash.
• evaluate_change: Scores risk and recommends feature flag usage.
• detect_flag: Discovers existing feature flags to avoid duplicates.
• wrap_change: Provides guidance on how to wrap a change in a feature flag.
• set_flag_rollout: Sets who gets a feature flag in each environment: rollout percentage, segments, constraints, and whether the environment is on.
• create_targeting: Creates the context fields and segments that set_flag_rollout targets, in a project or for every project.
• get_flag_state: Surfaces a feature flag's metadata and its activation strategies.
• get_flag_history: Returns a feature flag's recent changes from the event log, each as a diff of what changed, who changed it, when, and in which environment.
• evaluate_flag: Evaluates a single feature flag against an Unleash context (for example a user ID and custom properties) in an environment, and explains which strategies matched.
• list_flags: Lists all feature flags in a project, with optional pagination and sort order.
• search_flags: Searches active feature flags in all projects by part of their name or description, or finds one flag and its project by its full name.
• list_projects: Lists Unleash projects available to the configured token, with optional pagination.
• toggle_flag_environment: Enables or disables a feature flag in an environment.
• remove_flag_strategy: Deletes a feature flag's strategy from an environment.
• update_flag_lifecycle: Completes, uncompletes, archives, or revives a feature flag, or marks it as stale.
• update_project_settings: Sets a project's default stickiness (Enterprise only) and the default strategy of each environment, and adds existing environments to the project.
• cleanup_flag: Generates instructions for safely removing flagged code paths.
• send_feedback: Reports a failed tool call, an unsupported request, or an unexpected result to Unleash so the MCP can be improved. Requires the user's consent. Not listed when consent is denied up front through UNLEASH_MCP_SEND_FEEDBACK=false or the feedbackConsent server option.

Core workflow

The core workflow for an AI assistant is designed to be:

1. evaluate_change: First, assess a code change to see if a flag is needed.
2. detect_flag: This is often called automatically by evaluate_change to prevent creating duplicate flags.
3. create_flag: If a new flag is required, this tool creates it in Unleash.
4. wrap_change: Finally, this tool provides the language-specific code to implement the new flag.

See more information on the core workflow tools in the Tool reference section.

Prerequisites

Before you can run the server, you need the following:

• Node.js 22 or higher
• pnpm package manager or npm
• An Unleash instance (hosted or self-hosted)
• A personal access token with permissions to create feature flags

Get started

This section covers the different ways to install and run the Unleash MCP server. You can either follow a setup for agents (such as Claude Code and Codex), run the MCP as a standalone process using npx, or use a local development setup.

Agent setup

You can add the MCP server directly to Claude Code or Codex. Agent configurations are path-specific. You must run the following command from the root directory of the project where you want to use the MCP.

For Claude Code:

claude mcp add unleash \
    --env UNLEASH_BASE_URL={{your-instance-url}} \
    --env UNLEASH_PAT={{your-personal-access-token}} \
    -- npx -y @unleash/mcp@latest --log-level error

For Codex:

codex mcp add unleash \
    --env UNLEASH_BASE_URL={{your-instance-url}} \
    --env UNLEASH_PAT={{your-personal-access-token}} \
    -- npx -y @unleash/mcp@latest --log-level error

Remote agent setup (experimental)

Instead of running the MCP server locally, you can connect directly to your Unleash instance's built-in remote MCP server over HTTP. This uses the Streamable HTTP transport — no local process needed.

Note: Remote MCP is an experimental feature that must be enabled on your Unleash instance. Contact the Unleash team to get it enabled.

OAuth

The OAuth flow opens your browser, lets you log in to Unleash, and automatically provisions a short-lived PAT. No manual token management required.

For Claude Code:…