Connect an AI assistant over MCP

{
  "mcpServers": {
    "tuneplane": {
      "url": "https://tuneplane.your-company.com/api/mcp/rpc"
    }
  }
}

Paste that into your client’s MCP configuration. On first connect it opens a browser for authorisation, and then you can ask about a job directly:

Analyze the reward curve and KL of run-4f2a91, compare it against the previous run of the same
experiment, and suggest the next tuning step.

The console’s MCP page generates the exact snippet for your client, which is easier than assembling it by hand.

Set it up

Open the MCP page

Console → MCP. It is a premium feature, so the entry may not be there.

Copy the snippet for your client

Cursor, Claude, VS Code and others each want it in a slightly different place. The page shows the right shape for each.

Authorise

The client opens a browser for OAuth 2.1 with PKCE when it connects. There is no token to paste.

Pick which tools are on

Read tools are on by default. Write tools are off until you turn them on.

What the assistant can read

Group	Tools
Jobs	`list_jobs` `get_job` `get_metrics` `get_metrics_summary` `get_logs` `get_hardware` `get_environment` `get_validation_samples`
Configuration and comparison	`get_job_config` `get_hyperparams` `compare_jobs` `get_job_diagnosis` `get_project_memory` `list_projects`
Platform surface	`list_methods` `get_benchmarks` `get_sweep` `list_servings`

list_methods is the one worth knowing about: with a method name it returns the full hyperparameter declaration including types and ranges, so an assistant proposing a change can check it is legal before suggesting it.

What it can write, once you allow it

Tool	What it does
`stop_job`	Stops one of your running or queued jobs
`submit_job`	Clones a job’s code and profile, applies overrides, and submits it. Consumes quota
`create_submit_plan`	Puts a set of changes in the approval queue. Nothing is submitted until somebody approves it in the console

submit_job spends GPU hours. Quota and validation still apply, and the platform produces a submission plan for confirmation first, but an assistant with this tool on can queue work. Think twice on a shared deployment.

create_submit_plan is the safer shape for the same intent: the assistant proposes, a person approves.

Check it worked

Ask your client to list its tools. You should see the read tools above and nothing else, unless you enabled a write tool. Then ask for something concrete: get_metrics_summary on a finished run returns each metric’s latest value with its min and max and the step each happened at. If the client reports “not authenticated: complete the Connect / login authorization in your MCP client”, the OAuth round trip did not finish.

Endpoint and protocol

Transport is Streamable HTTP MCP, protocol revision 2026-07-28.
The same endpoint serves modern clients, which discover with server/discover and no handshake, and older clients that send initialize on 2025-11-25 and earlier.
Authorisation is OAuth 2.1 with PKCE. Client ID Metadata Documents are preferred, and Dynamic Client Registration stays as a fallback. Authorisation-code redirects carry RFC 9207 iss, which is skipped only in local TUNEPLANE_NO_AUTH=1 debugging.

The MCP page · Hugging Face · Job detail

Get started

Core concepts

Train a model

Evaluate and serve

Agent environments

Extend the platform

Collaborate

Integrations

Reference

Help

Connect an AI assistant over MCP

Set it up

What the assistant can read

What it can write, once you allow it

Check it worked

Endpoint and protocol

Next

​Set it up

​What the assistant can read

​What it can write, once you allow it

​Check it worked

​Endpoint and protocol

​Next

Set it up

What the assistant can read

What it can write, once you allow it

Check it worked

Endpoint and protocol

Next