Setup the MCP server

The Tuner MCP server lets your AI assistant read and write your Tuner workspace, so you can configure an agent — outcomes, intents, evals, red flags, alerts — by describing what you want instead of filling in forms.

The Tuner MCP Server is currently in Beta. Available actions and supported platforms may expand over time.

Why use it

Speed. Describe what you need in plain language and it’s done. Five call outcomes, ten evals, and a set of user intents in a single message — the fastest way to get an agent configured. Context. Your assistant already has your codebase, your agent’s system prompt, and your call flows. So instead of starting from a blank form, you can say “based on my agent’s system prompt, suggest the right call outcomes and evals” and get configuration that reflects how your agent actually works.

Supported platforms

Platform	Auth	Subscription	Setup time
Claude (claude.ai)	OAuth	Any	~2 minutes
Claude Code (terminal)	API key	Pro or higher	~2 minutes
Cursor	API key	Any	~3 minutes
Antigravity	API key	Any	~2 minutes

Tuner MCP Server: https://api.usetuner.ai/mcp/

IDEs

IDEs authenticate with an API key sent as a Bearer token. Copy yours from Workspace Settings → API Keys & Integrations.

Cursor

Open MCP Settings

Go to Settings → Tools & MCP and click Add Custom MCP to open your mcp.json.

Paste the Configuration

Add the following to your mcp.json:

{
  "mcpServers": {
    "tuner": {
      "type": "streamableHttp",
      "url": "https://api.usetuner.ai/mcp/",
      "headers": {
        "Authorization": "Bearer YOUR_TUNER_API_KEY"
      }
    }
  }
}

Replace YOUR_TUNER_API_KEY with your Tuner API key.

Claude Code

Run the Add Command

Open your terminal inside Claude Code and run:

claude mcp add tuner https://api.usetuner.ai/mcp/ --transport http --header "Authorization: Bearer YOUR_TUNER_API_KEY"

Replace YOUR_TUNER_API_KEY with your Tuner API key.

Antigravity

Open MCP Configuration

Click the … dropdown at the top of the editor’s agent panel, then Manage MCP Servers → View raw config to open mcp_config.json.

Paste the Configuration

Add the following to your mcp_config.json:

{
  "mcpServers": {
    "tuner": {
      "serverUrl": "https://api.usetuner.ai/mcp/",
      "headers": {
        "Authorization": "Bearer YOUR_TUNER_API_KEY",
        "Content-Type": "application/json"
      }
    }
  }
}

Replace YOUR_TUNER_API_KEY with your Tuner API key.

Chatbots

Chatbots authenticate with OAuth. The first time you add the server you’ll be redirected to Tuner to sign in and authorize access; after that your session stays active until you revoke it.

Claude

Add Custom Connector

Go to Settings → Connectors, click Add Custom Connector, and paste the Tuner MCP Server URL:

https://api.usetuner.ai/mcp/

Connect and Authenticate

Click Connect. You will be redirected to Tuner to sign in and authorize access.

Enable Tuner in Your Chat

When starting a new chat, click the + button and verify Tuner is toggled on under Connectors.

Start the setup

Once connected, paste this into your chat:

Set up Tuner observability for this agent using the Tuner MCP.
Fetch and follow the 'tuner_setup_agent' prompt from the Tuner MCP server.

If your assistant asks which agent or workspace to configure, you’ll find those details in Workspace Settings on the Tuner dashboard.

The tuner_setup_agent prompt walks your assistant through defining your call outcomes, intents, data-extraction fields, evals, red flags, and alerts, all from a single conversation.

Example prompts

Bulk setup from a description:

“I have a restaurant booking agent. Create the following call outcomes: Reservation Booked (Success), Reservation Modified (Success), Customer Cancelled (Failure), No Availability (Failure), and General Inquiry (Success). Also add user intents for: Make a Reservation, Modify a Reservation, Cancel a Reservation, and Ask About Menu.”

Several evals at once:

“Add these evals: Did the agent confirm the reservation date and time? Did the agent ask for the party size? Did the agent offer alternative times when the requested slot was unavailable?”

Reading your current setup:

“List all agents in my workspace and show me a summary of how each one is configured.”

Best practices

Be specific, and name the agent

Detail gets better results. Instead of “add some call outcomes”, try “for my Customer Support agent, add a call outcome called ‘Appointment Booked’ with type Success, defined as: the customer scheduled an appointment with a confirmed date and time.” If you have multiple agents, always say which one.

Use bulk operations for initial setup

Describe all your outcomes, intents, and evals in one message. The MCP processes them together, which is far faster than creating them one at a time in the dashboard.

Review changes after making them

Ask your assistant to list the current settings afterwards, so you can verify everything looks right before calls start flowing.

Use the dashboard for monitoring

The MCP is best for configuration and diagnosis. For tracking live results and trends over time, the Tuner dashboard is the better tool.

Troubleshooting

Connection failed or timed out

Chatbots: Verify you’re using the correct MCP Server URL. Ensure your Tuner account is active and you have the required subscription.
IDEs: Check that your API key is valid and correctly placed in the configuration file. Make sure https://api.usetuner.ai/mcp/ is reachable.

Authentication error

Chatbots: Try disconnecting and reconnecting. In Claude, go to Settings → Connectors, remove Tuner, then add it again.
IDEs: Regenerate your API key from the Tuner dashboard and update your configuration file.

Tuner not appearing in chat

Claude: Click the + button and check that Tuner is toggled on under Connectors.
IDEs: Verify the MCP server configuration is saved correctly and restart the IDE if needed.

What’s next?

Diagnose your agent

Once calls are flowing, use the MCP to find what’s broken, understand why, and fix it.

Call outcomes and intents

What you’re configuring when you set these up via MCP.

Custom evals

How the evals you create through your assistant are defined and scored.

Monitoring best practices

A routine for catching issues early.

Getting Started

Integrations

Evaluation

Observability

Simulation

MCP

Guides

Resources

Why use it

Supported platforms

IDEs

Cursor

Claude Code

Antigravity

Chatbots

Claude

Start the setup

Example prompts

Best practices

Troubleshooting

What’s next?

Diagnose your agent

Call outcomes and intents

Custom evals

Monitoring best practices

​Why use it

​Supported platforms

​IDEs

​Cursor

​Claude Code

​Antigravity

​Chatbots

​Claude

​Start the setup

​Example prompts

​Best practices

​Troubleshooting

​What’s next?

Diagnose your agent

Call outcomes and intents

Custom evals

Monitoring best practices

Why use it

Supported platforms

IDEs

Cursor

Claude Code

Antigravity

Chatbots

Claude

Start the setup

Example prompts

Best practices

Troubleshooting

What’s next?