krauncher-mcp

An MCP server that gives an agent one tool: a pre-run cost estimate for a GPU task, from static analysis of the code. The code is never executed.

It wraps the Krauncher analyzer (the assay), not the broker — there is no dispatch, no execution, no market lineup. Just: how long will this cost, and what does it need to run.

The seconds are a relative signal for comparing code against code, not an absolute forecast. They are normalized to a fixed reference card (RTX PRO 6000 WS) so two estimates are comparable; the GPU and host your code actually runs on will differ, so never read a second-count as the wall-clock you will get. Compare variant-to-variant. This is an early 0.x release — the model is approximate and evolving.

The estimate tool

Input: code — the task's Python source (a self-contained function; a @client.task-decorated function is fine).

Output, on the reference card (RTX PRO 6000 WS, the card CU is normalized to):

{
  "reference_card": "RTX PRO 6000 WS",
  "compute_sec": 20.2,      // the three phases of wall time on the ref card
  "setup_sec": 3.0,
  "io_sec": 2.1,
  "min_vram_gb": 6,         // raw requirement, no headroom margin
  "min_disk_gb": 10,
  "confidence": 1.0,        // 0-1
  "analysis_method": "ast", // "ast" | "llm"
  "cpu_only": false,
  "findings": [             // what the analyzer read from the code
    "num_epochs=1", "batch_size=16",
    "Recognized model: BERT Base (0.11B params)",
    "precision=fp16 from fp16=True"
  ]
}

The loop it is built for: edit the run → estimate → keep what's cheaper → repeat, all before spending a GPU-second. The estimate is a static forecast, not a guarantee; confidence and analysis_method say how much to trust it, and a rough estimate never blocks — it returns a best effort.

What it does not return: the cost model's calibration coefficients or weights. Only what the analyzer detected in the code leaves the server.

Install

pip install -e .        # from this directory; also installs the analyzer client

The server needs an API key for the analyzer, in the environment:

export CAS_API_KEY=cas_...

Verify it works without wiring up a client — runs estimate on a sample task and prints the contract:

krauncher-mcp --selftest

Wire it into an MCP client

stdio transport; the console script is krauncher-mcp.

{
  "mcpServers": {
    "krauncher-analyzer": {
      "command": "krauncher-mcp",
      "env": { "CAS_API_KEY": "cas_..." }
    }
  }
}

Scope

v1 is deliberately one tool. The per-GPU market lineup is intentionally left out — the agent's job is to improve its code and know the cost before running, and a pre-run estimate (even rough or partial) is the whole point.