Agent quickstart | Gridline

Overview

Gridline's MCP server gives any MCP client (Claude Code, Claude Desktop and others) tools for the whole loop: upload data, fine-tune, evaluate the result against its base, batch on the winner and download the merged weights. Each tool wraps one OpenAI-compatible route under Gridline's base URL, so an agent without MCP can do the same over HTTP.

https://api.gridlinegpu.com/openai/v1

Connect an agent

Create an API key in the dashboard and give it to the server as GRIDLINE_TOKEN. The key stays on your machine: the server sends it only to Gridline's API, never prints it and never returns it in a tool result.

Claude Code:

claude mcp add gridline --env GRIDLINE_TOKEN=$GRIDLINE_TOKEN -- npx -y @gridlinegpu/mcp

Any MCP client that reads a JSON config:

{
  "mcpServers": {
    "gridline": {
      "command": "npx",
      "args": ["-y", "@gridlinegpu/mcp"],
      "env": { "GRIDLINE_TOKEN": "<your Gridline API key>" }
    }
  }
}

The server runs over stdio as gridline-mcp.

A worked loop

Ask the agent in plain language, for example:

Fine-tune qwen2.5-7b-instruct on ./train.jsonl. Then compare the fine-tuned model
with the base on ./eval.jsonl, and run ./requests.jsonl as a batch on whichever scores
higher. Tell me what each paid step will cost before you run it.

A capable agent then:

Uploads train.jsonl with upload_file, purpose fine-tune.
Checks the model's fine-tuning price, tells you, and starts the job with create_fine_tune, then polls get_fine_tune and list_fine_tune_events until it has an ft: model.
Defines an eval with create_eval, quotes a run on the base and on the ft: model with create_eval_run and dry_run, and runs both once you agree.
Compares the scores with get_eval_run and list_output_items.
Uploads requests.jsonl (purpose batch), runs it on the better model with create_batch, and saves the results with download_results.

Spending credit

Three tools spend credit: create_fine_tune, create_batch and create_eval_run (unless dry_run is true). Each refuses unless the call carries confirm_spend: true, so an agent cannot spend by accident: it has to read the quote or the price first, and a well-behaved agent tells you before it confirms. Every other tool is free. What each job costs is on the pricing page; fine-tunes are charged per training token, batches per input and output token, each with its minimum.

Tools

Tool	Spends credit	What it does
`list_models`	No	Base models you can batch or fine-tune on, and your ft: models.
`get_credit_balance`	No	Your credit balance and recent charges.
`upload_file`	No	Upload a local JSONL file (purpose fine-tune or batch).
`create_fine_tune`	Yes	Start a LoRA, QLoRA or DPO fine-tuning job.
`list_fine_tunes`	No	Your fine-tuning jobs.
`get_fine_tune`	No	A job's status, its quote and its ft: model once it succeeds.
`list_fine_tune_events`	No	A job's progress and loss.
`cancel_fine_tune`	No	Cancel a fine-tuning job.
`create_batch`	Yes	Run a batch on a base model or your ft: model.
`get_batch`	No	A batch's status, counts and output file ids.
`cancel_batch`	No	Cancel a batch.
`download_results`	No	Save a batch's output or error file to a local path.
`create_eval`	No	Define an eval: its item schema and graders.
`create_eval_run`	Yes	Run an eval, or quote it with dry_run.
`get_eval_run`	No	A run's status and per-grader results.
`list_output_items`	No	A run's graded rows.
`request_merged_weights`	No	Export an ft: model merged into its base.
`get_merged_weights`	No	A merged-weights export's status and files.
`get_merged_file_url`	No	A short-lived download URL for one merged file.

Plain HTTP

An agent without MCP can run the same loop with any HTTP client, using a Gridline API key as the bearer token. The routes and objects are OpenAI's (see the quickstart).

# 1. Upload training data
curl https://api.gridlinegpu.com/openai/v1/files -H "Authorization: Bearer $GRIDLINE_TOKEN" \
  -F purpose=fine-tune -F [email protected]

# 2. Fine-tune (the answer carries the job's quote)
curl https://api.gridlinegpu.com/openai/v1/fine_tuning/jobs -H "Authorization: Bearer $GRIDLINE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen2.5-7b-instruct", "training_file": "file-...", "method": "qlora"}'

# 3. Quote an eval run first (dry_run), then run it without dry_run
curl https://api.gridlinegpu.com/openai/v1/evals/eval_.../runs -H "Authorization: Bearer $GRIDLINE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"data_source": {...}, "dry_run": true}'

# 4. Batch on the fine-tuned model
curl https://api.gridlinegpu.com/openai/v1/batches -H "Authorization: Bearer $GRIDLINE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"input_file_id": "file-...", "endpoint": "/v1/chat/completions", "completion_window": "24h"}'