MCP 服务器

Featureflow MCP Server

Enables AI assistants to manage Featureflow feature flags, including creating and updating features, controlling feature states across environments, and managing projects, environments, and targeting rules through natural language.

README

Featureflow MCP Server

An MCP (Model Context Protocol) server for Featureflow feature flag management. This enables AI assistants like Claude to interact with your Featureflow instance to manage feature flags, projects, environments, and more.

Quick Start

1. Create a Personal Access Token

Log into Featureflow as an administrator
Navigate to Administration → API Tokens
Click Create Token and copy the token (starts with api-)

2. Configure in Cursor

Add to your ~/.cursor/mcp.json:

{
  "mcpServers": {
    "featureflow": {
      "command": "npx",
      "args": ["-y", "featureflow-mcp"],
      "env": {
        "FEATUREFLOW_API_TOKEN": "api-your-token-here"
      }
    }
  }
}

3. Restart Cursor

Press Cmd+Shift+P → "MCP: Restart Servers" or restart Cursor.

That's it! You can now ask Claude to manage your feature flags.

Configuration

Environment Variable	Description	Default
`FEATUREFLOW_API_TOKEN`	Personal Access Token (required)	-
`FEATUREFLOW_API_URL`	API base URL (optional)	`https://beta.featureflow.io/api`

Self-Hosted Featureflow

If you're running a self-hosted Featureflow instance:

{
  "mcpServers": {
    "featureflow": {
      "command": "npx",
      "args": ["-y", "featureflow-mcp"],
      "env": {
        "FEATUREFLOW_API_URL": "https://your-instance.com/api",
        "FEATUREFLOW_API_TOKEN": "api-your-token-here"
      }
    }
  }
}

Available Tools

Account

Tool	Description
`get_current_user`	Get the currently authenticated user and organization

Projects

Tool	Description
`list_projects`	List all projects, optionally filtered by query
`get_project`	Get a specific project by ID or key
`create_project`	Create a new project
`update_project`	Update an existing project
`delete_project`	Delete a project

Features

Tool	Description
`list_features`	List features with optional filters
`get_feature`	Get a specific feature by ID or unified key
`create_feature`	Create a new feature flag
`update_feature`	Update an existing feature
`clone_feature`	Clone a feature with a new key
`archive_feature`	Archive or unarchive a feature
`delete_feature`	Delete a feature

Feature Controls

Tool	Description
`get_feature_control`	Get feature control settings for an environment
`update_feature_control`	Enable/disable features, modify rules

Environments

Tool	Description
`list_environments`	List environments for a project
`get_environment`	Get a specific environment
`create_environment`	Create a new environment
`update_environment`	Update an existing environment
`delete_environment`	Delete an environment

Targets & API Keys

Tool	Description
`list_targets`	List targeting attributes for a project
`get_target`	Get a specific target by key
`list_api_keys`	List SDK API keys for an environment

Example Usage

Once configured, you can ask Claude things like:

"Who am I logged in as in Featureflow?"
"List all my Featureflow projects"
"Create a feature called 'new-checkout' in the 'webapp' project"
"Enable the 'dark-mode' feature in production"
"What features are currently enabled in staging?"
"Disable 'beta-feature' in all environments"

Development

# Clone the repository
git clone https://github.com/featureflow/featureflow-mcp.git
cd featureflow-mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
FEATUREFLOW_API_TOKEN=api-xxx npm start