SynqTune MCP server

Lets a coding agent (Claude Code, Cursor, Claude Desktop, or any MCP client) work with SynqTune: check runs, read comparison reports, estimate costs, and launch template pipelines in your own AWS account.

Setup

1. In SynqTune, open Settings → API tokens and create a token. Tokens are read-only unless you tick Can launch runs.
2. Add the server to your agent. You need uv installed.

Claude Code

claude mcp add synqtune \
  --env SYNQTUNE_API_TOKEN=sqt_... \
  --env SYNQTUNE_URL=https://synqtune.com \
  -- uvx synqtune-mcp

Other clients (JSON config):

{
  "mcpServers": {
    "synqtune": {
      "command": "uvx",
      "args": ["synqtune-mcp"],
      "env": { "SYNQTUNE_API_TOKEN": "sqt_...", "SYNQTUNE_URL": "https://synqtune.com" }
    }
  }
}

Without uv: pip install synqtune-mcp and use synqtune-mcp as the command. From a local checkout: uvx --from ./mcp_server synqtune-mcp.

Tools

Safety

• launch_pipeline returns the plan and cost estimate first. It only starts a run when called again with confirm=true, so the agent has to show you the plan before anything is billed.
• Runs use GPU instances in your AWS account, billed by AWS to you.
• Provider API keys are never passed through the MCP server. A comparison with your current LLM uses the key saved in SynqTune under Settings → Provider keys.
• Tokens can't create other tokens, read provider keys or change cloud accounts. Revoke a token in Settings and it stops working immediately.

Example

Train a ticket router on s3://my-bucket/tickets.jsonl with labels billing, refund, shipping, technical, and compare it with gpt-4.1-mini using our production prompt.

The agent calls launch_pipeline without confirming, shows you the steps, the task instruction and the estimated cost, and launches once you agree. Later, ask "is the router ready to replace GPT?" and it reads the comparison report.

Development

To release, bump version in pyproject.toml and push a tag mcp-v<version> (for example mcp-v0.1.1). The Publish MCP server workflow builds the package and publishes it to PyPI.

Templates in synqtune_mcp/templates.py mirror frontend/src/lib/task-templates.ts; change both together. Tests: python -m pytest tests/test_mcp_server.py from the repository root.