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DevCyle MCP Getting Started

The DevCycle Model Context Protocol (MCP) Server is based on the DevCycle CLI, it enables AI-powered code editors like Cursor and Windsurf, or general-purpose tools like Claude Desktop, to interact directly with your DevCycle projects and make changes on your behalf.

Quick Setup

The DevCycle MCP is hosted so there is no need to set up a local server. We'll walk you through installation and authentication with your preferred AI tools.

Direct Connection: For clients that natively support the MCP specification with OAuth authentication, you can connect directly to our hosted server:

https://mcp.devcycle.com/mcp

Protocol Support: Our MCP server supports both SSE and HTTP Streaming protocols, automatically negotiating the best option based on your client's capabilities.

Alternative Endpoint: If your client has issues with protocol negotiation, use the SSE-only endpoint:

https://mcp.devcycle.com/sse
info

These instructions use the remote DevCycle MCP server. For installation of the local MCP server, see the reference docs.


Configure Your AI Client

📦 Install in Cursor

To open Cursor and automatically add the DevCycle MCP, click the install button above. Alternatively, add the following to your ~/.cursor/mcp_settings.json file. To learn more, see the Cursor documentation.

{
"mcpServers": {
"DevCycle": {
"url": "https://mcp.devcycle.com/mcp"
}
}
}

Authentication in Cursor:

  1. After configuration, you'll see DevCycle MCP listed as "Needs login" with a yellow indicator
  2. Click on the DevCycle MCP server to initiate the authorization process
  3. This opens a browser authorization page at mcp.devcycle.com
  4. Review and click "Allow Access" to grant permissions
  5. If you have multiple organizations, select your desired organization at auth.devcycle.com
  6. You'll be redirected back to Cursor with the server now active

Available Tools

The DevCycle MCP Server provides comprehensive feature flag management tools organized into 6 categories:

CategoryToolsDescription
Feature Managementlist_features, create_feature, update_feature, update_feature_status, delete_feature, fetch_feature_variations, create_feature_variation, update_feature_variation, set_feature_targeting, list_feature_targeting, update_feature_targeting, get_feature_audit_log_historyCreate and manage feature flags, variations, and targeting
Variable Managementlist_variables, create_variable, update_variable, delete_variableManage feature variables
Environment Managementlist_environments, get_sdk_keysEnvironment configuration
Project Managementlist_projects, get_current_projectProject management
Self-Targeting & Overridesget_self_targeting_identity, update_self_targeting_identity, list_self_targeting_overrides, set_self_targeting_override, clear_feature_self_targeting_overridesTesting and overrides
Results & Analyticsget_feature_total_evaluations, get_project_total_evaluationsUsage analytics

Try It Out

Once configured, try asking your AI assistant:

  • "Create a new feature flag called 'new-checkout-flow'"
  • "List all features in my project"
  • "Enable targeting for the header-redesign feature in production"
  • "Show me evaluation analytics for the last 7 days"

Next Steps

Getting Help