Update to Tamarind Bio

{
  "name": "tamarind-mcp-inverse-folding",
  "description": "Design sequences for a fixed protein backbone or run protein language models for embeddings, mutation scoring, likelihoods, and sequence generation with Tamarind Bio through MCP. Use for ProteinMPNN-, LigandMPNN-, ESM-IF-, or PLM-style tasks. Not for de novo backbone generation, antibody CDR design, or ordinary folding.",
  "included_files": [],
  "skill_md_contents": "---\nname: tamarind-mcp-inverse-folding\ndescription: Design sequences for a fixed protein backbone or run protein language models for embeddings, mutation scoring, likelihoods, and sequence generation with Tamarind Bio through MCP. Use for ProteinMPNN-, LigandMPNN-, ESM-IF-, or PLM-style tasks. Not for de novo backbone generation, antibody CDR design, or ordinary folding.\n---\n\n# Run inverse folding and protein language models\n\nSeparate two task families: inverse folding starts from a 3D backbone and produces sequences; protein language models start from sequence and produce embeddings, scores, likelihoods, or variants.\n\n## Discover and validate\n\nCall `listTags` when needed, then query `getAvailableTools` for inverse folding, protein language models, or embeddings. Inspect the chosen tool with `getJobSchema`.\n\nFor inverse folding, confirm designed chains/residues, fixed context, ligand context, sequence count, temperature/noise, excluded residues, and model variant. For PLMs, confirm task, model size, sequence limit, output format, and scan or generation settings.\n\nUpload a backbone with `uploadFile` or pass an exact accepted upstream `s3Path`. Call `validateJob`, require no mutation warning, then call `estimateTime`. Surface model size, sequence count, temperature, batch size, and estimated spend.\n\n## Execute and verify\n\nUse `tamarind-mcp-submit-and-poll`. For multiple inputs or downstream folds, use `tamarind-mcp-batch` rather than looping over `submitJob`.\n\nInverse-folded sequences require structural validation. Refold a diverse bounded subset with `tamarind-mcp-structure-prediction`, then inspect backbone recovery and confidence/interface metrics. Pass designed sequences to the downstream schema's sequence field; do not place them in a template or structure-file field.\n"
}