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Version: Config V2

MCP Server

Overview​

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). The ConfigCat MCP server lets you manage feature flags and configurations via the Public Management API seamlessly from AI tools. It also enables your code editor to understand your feature flags, integrate the appropriate ConfigCat SDK into your project or even create new feature flags directly in your codebase.

Features​

  • Manage organizations, members and permissions
  • Create and update products, configs, and environments
  • Manage feature flags and settings
  • Manage tags and user segments
  • Connect with integrations and webhooks
  • Track activity with audit logs and zombie flag (stale flag) reports
  • Find code references linked to your features
  • Integrate ConfigCat SDK and implement feature flags in your project

Prerequisites​

Setup​

Supply your Public Management API basic auth credentials.

The MCP server's configuration includes the following environment variables:

VariableRequiredDefaultDescription
CONFIGCAT_API_USER☑–Management API basic auth username.
CONFIGCAT_API_PASS☑–Management API basic auth password.
CONFIGCAT_BASE_URLhttps://api.configcat.comOverride API host (rarely needed).

Client Configuration​

  1. Install Cursor
  2. Open Preferences → Cursor Settings → MCP & Integrations
  3. Click Add Custom MCP
  4. Add (or merge) the snippet below into your JSON settings:
{
  "mcpServers": {
    "ConfigCat": {
      "command": "npx",
      "args": ["-y", "@configcat/mcp-server"],
      "env": {
        "CONFIGCAT_API_USER": "YOUR_API_USER",
        "CONFIGCAT_API_PASS": "YOUR_API_PASSWORD"
      }
    }
  }
}
  1. Save – the server will start on demand.
  2. You can start writing prompts into Cursor's AI panel.
info

Replace YOUR_API_USER and YOUR_API_PASSWORD environment variables with your Public Management API basic auth credentials.

Interaction​

After you install the ConfigCat MCP server in your AI client, you can prompt your agent to create or manage your feature flags and configurations. Typically you need to click Run tool (or the equivalent option in your AI client) to execute the result.

For example, you could try asking

Create a boolean feature flag called "my_awesome_feature" in the "Backend" config

or

Turn the "my_awesome_feature" flag ON in all environments

or

Update the "my_awesome_feature” flag in dev environment so it’s only enabled for users in Canada

or

Create a new feature flag by cloning the configuration of the "my_awesome_feature" flag, and name it "my_another_awesome_feature".

or

Update the "my_awesome_feature" flag description: "When enabled, show my awesome feature.”

or

List the stale feature flags that haven’t been modified in the past 6 days.

or

Show who last modified the "myNewAwesomeFeature" flag, and to what.

or

Invite sarah@example.com to the "Main" product with "Administrators" permissions.

See Also​