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Each client registers the Kameleoon MCP server in its own configuration, then completes an OAuth login once. Follow the steps for your client, then run a sample prompt to confirm that the tools work.

Claude Code integration

Step 1: Register the server

To register the server for your user profile (making it available in all projects), open a terminal and run the following command:
To register the server only for the current project (which adds a .mcp.json file to your repository for team sharing), run:
Verify that Claude added the server:
Expected output: kameleoon: ... - ✗ Failed to connect (A failed connection here is normal because you haven’t completed authentication yet).

Step 2: Complete OAuth authentication

In the same terminal, trigger the login flow:
  1. The command automatically opens a browser tab. If it doesn’t, copy the URL printed in the terminal and open it manually.
  2. Sign in to your Kameleoon account.
  3. Click Authorize.
  4. When the browser displays a success message, press Ctrl+C in your terminal.
Verify the connection:
Expected output: kameleoon: ... - ✓ Connected

Step 3: Start a new Claude Code session

Claude Code makes tools from newly added MCP servers available only in sessions that you start after registering the server. Close your current Claude Code chat and open a new one.

Step 4: Verify the connection

In a new Claude Code conversation, try the following prompts:
  • “List the Kameleoon feature flags.”
  • “What experiments are running on site X?”
  • “Show the status of experiment Y.”
  • “Show the code for variation 1 of experiment Z.”

Antigravity integration

Quick setup

Paste the following self-starter prompt directly into your Antigravity chat to connect automatically:

Manual configuration

Edit the configuration file at ~/.gemini/config/mcp_config.json and add the following JSON block. If the file already has an mcpServers object, add only the kameleoon entry to it:

Codex integration

Quick setup

Paste the following self-starter prompt into your Codex chat:

Manual configuration

Add the following block to ~/.codex/config.toml. Create the file if it doesn’t exist:

Authenticate the connection

The Kameleoon MCP server uses OAuth. Run the following command in your terminal to start the authorization flow:
Expected behavior:
  1. The browser automatically opens a window.
  2. Click Authorize on the Kameleoon login page.
  3. Kameleoon completes the local callback on port 35535.
  4. The terminal confirms that the proxy connected successfully.
A successful connection outputs text similar to the following:

Verify tool operations

After you authenticate, run the following checks to confirm the tools work as expected:
  1. List available tools: Confirm that tools/list succeeds and returns the Kameleoon tools. Ensure the output includes the tools listed in the tool reference, such as experiment_code_get, feature_flag_list, and feature_flag_create.
  2. Retrieve feature flags: Run feature_flag_list(siteCode = "d1alzzxd7k"). A successful response returns a list of feature flags for the specified site.
  3. Retrieve experiment results: Run experiment_results_get(experimentId = 149640). A successful response includes the results for each goal attached to the experiment.

Cursor integration

Cursor integrates MCP tools directly into the IDE Chat sidebar, which makes them available while you code.
  1. Open Cursor Settings (Cmd+Shift+J on macOS, Ctrl+Shift+J on Windows/Linux).
  2. Navigate to Features > MCP Servers > + Add New MCP Server.
  3. Set the Name to kameleoon.
  4. Set the Type to command.
  5. Enter the following string as the Command:
  6. Save the configuration.

Option 2: Configure in a JSON file (advanced)

Open ~/.cursor/mcp.json (create the file if it doesn’t exist) and add the following entry to the mcpServers object:
Restart Cursor after you edit the file manually.

Sample prompts for developer workflows

After you connect the Kameleoon MCP server, use prompts like the following in your IDE:
  • “List the Kameleoon MCP tools available in this session.”
  • “Show all feature flags for site code d1alzzxd7k.”
  • “Get the details for feature flag new_search on site d1alzzxd7k.”
  • “Fetch experiment results for experiment 149640 and summarize the current status.”
  • “Pull the variation code for experiment <experimentId> and variation <variationId>.”
For more advanced workflows, try the following prompts:
  • “Inspect feature flag new_search for site d1alzzxd7k and explain what environments and variations it has.”
  • “Summarize experiment 149640 for an engineer. Include status, site code, winner state, and whether any variation data is available.”
  • “List the active feature flags for site d1alzzxd7k and point out any flags that look like stale candidates.”
  • “Retrieve the code for variation <variationId> in experiment <experimentId> and explain what frontend behavior it changes.”
  • “Create a new feature flag named <name> with key <featureKey> for site d1alzzxd7k.”
  • “Turn on feature flag <featureKey> in the staging environment for site d1alzzxd7k.”
  • “Turn off feature flag <featureKey> in the production environment for site d1alzzxd7k.”
  • “How has the checkout experiment performed week by week on phones?”
  • “Did new visitors react differently from returning ones in the pricing test? Use Bayesian statistics.”
  • “Show the daily trend of the checkout test without Safari, counting every purchase, not only buyers.”
  • “Create a segment of returning visitors from Germany on desktop who already completed the Purchase goal.”

Prompting tips

  • Include the site code when you work with feature flags.
  • Include the experiment ID when you query experiments or experiment results.
  • Include both experimentId and variationId when you request variation code.
  • Explicitly name the target environment when you ask the agent to turn a feature flag on or off.
  • Request a plain-English summary if you want your AI agent to interpret the MCP response rather than just retrieving the raw data.