Update to Tamarind Bio

{
  "name": "tamarind-mcp-finetune",
  "description": "Fine-tune a supported Tamarind-hosted model on labeled data and run its matching inference tool through MCP. Use for live account-visible finetune/inference pairs in protein, affinity, enzyme, or small-molecule workflows. Not for stock inference, ordinary batch prediction, or unsupported custom training code.",
  "included_files": [],
  "skill_md_contents": "---\nname: tamarind-mcp-finetune\ndescription: Fine-tune a supported Tamarind-hosted model on labeled data and run its matching inference tool through MCP. Use for live account-visible finetune/inference pairs in protein, affinity, enzyme, or small-molecule workflows. Not for stock inference, ordinary batch prediction, or unsupported custom training code.\n---\n\n# Fine-tune and run inference through MCP\n\nTreat training and inference as two durable, separately validated jobs with explicit data and evaluation boundaries.\n\n## Confirm a supported pair\n\nCall `getAvailableTools(function=\"finetuning\")` or use a narrow `search`. Inspect both the training and inference tools with `getJobSchema`. Require the live schemas to document the handoff; do not infer a pair from names alone.\n\n## Prepare training\n\nCheck required columns, target units, identifiers, split strategy, class balance, leakage, held-out evaluation, and size limits. Upload the dataset with `uploadFile` and use the returned bare filename.\n\nCall `validateJob` for the training payload, require no mutation warning, and call `estimateTime`. Surface the base model, epochs or steps, dataset size, split, expected runtime, and weighted hours when available. Obtain explicit authorization because training can be materially expensive.\n\n## Train and infer\n\nRun training with `tamarind-mcp-submit-and-poll`. Require an explicit success state. Inspect `listJobFiles` and the training row for the exact trained-model reference required by the inference schema.\n\nBuild and validate the inference payload with that exact reference. Estimate and separately authorize inference when it materially expands scope, then run it through `tamarind-mcp-submit-and-poll` or `tamarind-mcp-batch` for many independent inputs.\n\nEvaluate on held-out data and compare against the stock/base model. Report leakage risks, calibration limits, dataset applicability, and uncertainty before using predictions downstream.\n"
}