Model Context Protocol (MCP)

veri mcp serve runs a Model Context Protocol server over stdio that exposes Veri as a set of tools. MCP clients such as Claude Code, Claude Desktop, and Cursor can inspect deployment metrics and request failures, query bounded training metrics, check account burn, compare eval runs, and—when writes are enabled—submit or cancel runs on your behalf. The server runs on your own machine. Your MCP client starts it as a subprocess and talks to it over stdin/stdout, so there is nothing to host and no port to open. It authenticates with the same credentials the CLI uses, so it can do exactly what you can do from the CLI, nothing more.

Setup

Install the SDK with the mcp extra

The MCP server ships with the SDK behind an optional mcp extra:

uv tool install 'veri-sdk[mcp]'

pipx install 'veri-sdk[mcp]'

pip install 'veri-sdk[mcp]'

Confirm the command is available:

veri mcp serve --help

The server reads your stored credentials, so log in once:

veri login

You never paste an API key into the MCP client. The server uses the credentials from veri login (or the VERI_API_KEY environment variable if you set one).

Add Veri to your MCP client. The command the client runs is always veri mcp serve.Claude Code has a one-liner. The part after -- is the command it will spawn:

claude mcp add veri -- veri mcp serve

By default this registers Veri for you in the current project. Use -s user to make it available in every project, or -s project to write a .mcp.json that your whole team shares (each teammate still runs their own veri login).Claude Desktop and Cursor are configured with a JSON file. Add a veri entry under mcpServers:

{
  "mcpServers": {
    "veri": {
      "command": "veri",
      "args": ["mcp", "serve"]
    }
  }
}

The file location depends on the client:

Client	Config file
Claude Desktop (macOS)	`~/Library/Application Support/Claude/claude_desktop_config.json`
Claude Desktop (Windows)	`%APPDATA%\Claude\claude_desktop_config.json`
Cursor (global)	`~/.cursor/mcp.json`
Cursor (per project)	`.cursor/mcp.json` in the project root

Restart and verify

Restart the client so it picks up the new server.In Claude Code, run claude mcp list (or /mcp inside a session) and confirm veri shows as connected. In Claude Desktop and Cursor, the Veri tools appear once the server connects. Ask the agent something like “what is my Veri credit balance?” to confirm it can reach your account.

Read-only mode

By default every tool is available, including ones that create billable GPU jobs and deployments. Your MCP client prompts you to approve each tool call, so creation is gated by that approval. Tools that spend credits have descriptions that begin with CREATES A BILLABLE ... so the intent is clear in the approval prompt. For an automated or unattended setup where no human approves each call, run the server read-only so the creation and cancellation tools are not registered at all. Pass --readonly in the command:

claude mcp add veri -- veri mcp serve --readonly

{
  "mcpServers": {
    "veri": {
      "command": "veri",
      "args": ["mcp", "serve", "--readonly"]
    }
  }
}

Setting the environment variable VERI_MCP_READONLY=1 has the same effect.

Available tools

Read tools (always available)

Tool	What it does
`veri_list_training_jobs`	List training jobs, with optional status and method filters.
`veri_get_training_job`	Get one job: status, cost, dashboard URL, and any error.
`veri_list_deployments`	List deployments, with an optional status filter.
`veri_get_deployment`	Get one deployment: status, endpoint, GPU, and running cost.
`veri_get_deployment_metrics`	Get compact lifetime request, token, percentile latency, error, uptime, and cost metrics.
`veri_query_deployment_metrics`	Query up to 500 server-aggregated metric buckets for a time range (seven days by default).
`veri_list_deployment_requests`	List compact request telemetry, optionally restricted to failures or a time range.
`veri_get_training_metrics`	Get bounded native training curves and the W&B run URL when available.
`veri_list_models`	List custom models in your registry.
`veri_list_datasets`	List datasets available for training.
`veri_list_regions`	List the launch regions you can target.
`veri_estimate_cost`	Estimate the cost of a GPU job before submitting it.
`veri_billing_balance`	Get your current credit balance.
`veri_get_billing_overview`	Get balance, training and deployment burn, runway, active resources, and recent transactions.
`veri_get_cost_summary`	Get the supported historical cost breakdown for up to 30 days.
`veri_list_benchmarks`	List the curated benchmark catalog.
`veri_list_evals` / `veri_get_eval`	List or inspect evaluation definitions.
`veri_list_eval_runs` / `veri_get_eval_run`	List or inspect evaluation runs, results, usage, and gates.
`veri_list_eval_run_items`	Inspect bounded per-item outputs and scores.
`veri_compare_eval_runs`	Compare a candidate eval run with its baseline.
`veri_chat_deployment`	Send a chat request to a running deployment.

Write tools (available unless read-only)

Tool	What it does
`veri_create_training_job`	Submit a managed training run. Returns the job and a pre-submit cost estimate.
`veri_create_deployment`	Create an inference deployment from a training job or a HuggingFace model.
`veri_cancel_training_job`	Cancel a queued or running job.
`veri_stop_deployment`	Stop a running deployment.
`veri_register_model`	Register a completed job’s checkpoint as a reusable custom model.
`veri_create_eval`	Create an evaluation definition.
`veri_start_eval_run`	Start a billable evaluation run after confirming model and sample count.
`veri_cancel_eval_run`	Cancel an evaluation run.
`veri_rescore_eval_run`	Rescore stored outputs; paid model scorers may incur cost.

Test deployment observability with MCP

MCP is optional for deployment observability—the dashboard, SDK, and REST API expose the same underlying telemetry. MCP is the agent-facing interface for investigating that telemetry in natural language. For a safe test, register the server in read-only mode:

claude mcp add veri-observe -- veri mcp serve --readonly

Send a buffered and a streaming request to a serving deployment, then ask:

Inspect deployment dep_... over the past hour. Report request volume, p50/p95/p99 TTFT and end-to-end latency, token throughput, error rate, and cost efficiency. List failed requests grouped by error category. Do not modify resources.

The agent should use:

veri_get_deployment_metrics for compact lifetime health and cost context.
veri_query_deployment_metrics for server-aggregated time-series buckets.
veri_list_deployment_requests for the terminal records behind errors.

Time-series queries return at most 500 buckets, and request-history queries return at most 50 rows per call. Pass an explicit deployment ID and time window when you want repeatable results.

Deployment observability smoke test

Generate buffered and streaming traffic, verify dashboard charts, test SDK and REST queries, and interpret every metric.

Training analysis with W&B

Use Veri MCP for infrastructure, lifecycle, deployment telemetry, billing, and eval comparisons. Connect the official W&B MCP alongside it for rich cross-run training analysis, artifacts, and reports. Veri returns a bounded native training metric series and the W&B run URL when available. It does not proxy W&B credentials or reimplement W&B’s query surface.

Working with datasets and reward functions

The server does not upload local files. Upload your dataset and reward functions with the CLI first, then pass their ids to the agent:

veri datasets upload ./data/train.jsonl --name my-data
veri rewards upload ./reward.py --name my-reward

The agent can then reference my-data and the reward id when it calls veri_create_training_job.

Get started

Training

Deployments

Evaluations

CLI

Volumes

Setup

Read-only mode

Available tools

Read tools (always available)

Write tools (available unless read-only)

Test deployment observability with MCP

Deployment observability smoke test

Training analysis with W&B

Working with datasets and reward functions

​Setup

​Read-only mode

​Available tools

​Read tools (always available)

​Write tools (available unless read-only)

​Test deployment observability with MCP

Deployment observability smoke test

​Training analysis with W&B

​Working with datasets and reward functions

Setup

Read-only mode

Available tools

Read tools (always available)

Write tools (available unless read-only)

Test deployment observability with MCP

Training analysis with W&B

Working with datasets and reward functions