Unleash MCP Server Integration

What does “Unleash” MCP Server do?

The Unleash MCP Server is a Model Context Protocol (MCP) implementation that connects AI assistants and LLM applications to the Unleash Feature Toggle system. It acts as a bridge, enabling AI clients to query feature flag statuses, list projects, and manage feature flags directly from Unleash via standardized MCP interfaces. This integration allows developers to automate feature management, expose feature flag data to AI agents for informed decisions, and streamline workflows that depend on dynamic feature toggling in software systems. By providing tools and resources that interact with Unleash, the server empowers AI-driven applications to enhance development pipelines, run automated checks, and participate in feature management operations.

List of Prompts

• flag-check: A prompt template for checking the status of a single feature flag in Unleash.

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List of Resources

• flags: Exposes feature flag data as an MCP resource, allowing clients to read and use feature flag information as context.
• projects: Allows clients to access and list all projects configured within the Unleash system.

List of Tools

• get-flag: A tool that retrieves the status of a specified feature flag from Unleash.
• get-projects: A tool that lists all available projects from the Unleash server.

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Use Cases of this MCP Server

• Feature Flag Monitoring: Allow AI agents to programmatically check the status of feature flags, enabling dynamic decision-making in workflows and automated testing scenarios.
• Automated Feature Management: Use AI to create, update, or manage feature flags based on contextual signals or deployment requirements.
• Project Discovery: Easily list and explore available projects within Unleash, streamlining project onboarding and integration for teams.
• Contextual Flag Exposure for LLMs: Expose feature flag information as context to language models, enabling more nuanced responses and operational awareness.
• Continuous Deployment Integration: Automate feature flag toggling and project management as part of CI/CD pipelines, increasing agility and reducing manual intervention.

How to set it up

Windsurf

1. Ensure Node.js (v18+) is installed.
2. Locate your Windsurf configuration file.
3. Add the Unleash MCP server to the mcpServers object using the following JSON snippet:

   "mcpServers": {
     "unleash-mcp": {
       "command": "npx",
       "args": ["@cuongtl1992/unleash-mcp@latest"]
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify that the Unleash MCP server is running in the Windsurf dashboard.

Securing API Keys

Use environment variables to store sensitive information:

{
  "mcpServers": {
    "unleash-mcp": {
      "command": "npx",
      "args": ["@cuongtl1992/unleash-mcp@latest"],
      "env": {
        "UNLEASH_API_KEY": "${UNLEASH_API_KEY}"
      },
      "inputs": {
        "apiUrl": "https://unleash.example.com/api"
      }
    }
  }
}

Claude

1. Install Node.js (v18+) if not present.
2. Open the Claude configuration file.
3. Add Unleash MCP to the mcpServers section:

   "mcpServers": {
     "unleash-mcp": {
       "command": "npx",
       "args": ["@cuongtl1992/unleash-mcp@latest"]
     }
   }
   

4. Save the file and restart Claude.
5. Confirm successful integration via Claude’s tools menu.

Cursor

1. Make sure Node.js (v18+) is installed.
2. Find and edit the Cursor configuration file.
3. Insert the following MCP server configuration:

   "mcpServers": {
     "unleash-mcp": {
       "command": "npx",
       "args": ["@cuongtl1992/unleash-mcp@latest"]
     }
   }
   

4. Save the configuration and restart Cursor.
5. Check the MCP server status in Cursor.

Cline

1. Check that Node.js (v18+) is available.
2. Access the Cline configuration file.
3. Add Unleash MCP server details as shown:

   "mcpServers": {
     "unleash-mcp": {
       "command": "npx",
       "args": ["@cuongtl1992/unleash-mcp@latest"]
     }
   }
   

4. Restart Cline after saving.
5. Validate the Unleash MCP server functionality.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "unleash-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "unleash-mcp" to your MCP server’s actual name and replace the URL accordingly.

Overview

Our opinion

Unleash MCP Server provides a clear, focused integration for feature flag management in LLM workflows. The repository covers all essential MCP primitives, offers practical setup instructions, and demonstrates good security practices. However, advanced MCP features like sampling and roots are not explicitly documented. Overall, it is a solid, specialized MCP server with clear developer value.

MCP Score