Tamarind Bio — validated examples & output shapes

The freshest example for any tool is the exampleJob field MCP getJobSchema(<tool>) now returns — an {jobName, type, settings} built from each param's example/default (with an exampleJobNote; org-gated params you can't use are omitted, file params get placeholder filenames). It's the best starting point, but run validateJob on it before submitting — it's assembled from per-param examples, not a guaranteed-valid payload, so a given tool's exampleJob can need a tweak. The payloads below are a validateJob-confirmed fallback for REST callers or when you want a worked example. Tool schemas evolve — if one stops validating, re-fetch with getJobSchema(<tool>) / GET /tools. Sequences here are illustrative; swap your own.

File params (proteinFile, pdbFile, ligandFile, …) need a real file value — either the bare filename of an uploaded file (target.pdb — NOT email-prefixed), a prior-job output path (JobName/out/x.pdb), or inline PDB/SDF-format text (multi-line ATOM/HETATM records). The <...> placeholders below are NOT valid as written — replace them. Do not put an amino-acid sequence in a file param — validateJob rejects it with File ... must be of types: ["pdb"]. (A sequence goes in sequence, a structure goes in a file param.)

BASE = "https://app.tamarind.bio/api", HEADERS = {"x-api-key": <key>}.

Self-check (run this first to confirm the skill works for you)

Read-only + dry-run, no submission, no cost. Confirms the discover → schema → validate loop end-to-end:

python

import os, requests
BASE, HEADERS = "https://app.tamarind.bio/api", {"x-api-key": os.environ["TAMARIND_API_KEY"]}

# 1. discovery reachable?
tools = requests.get(f"{BASE}/tools", headers=HEADERS).json()
assert isinstance(tools, list) and any(t["name"] == "alphafold" for t in tools), "tools endpoint"

# 2. validate a known-good payload (MCP validateJob; or skip if REST-only)
#    expect {"valid": true, ...}

With the MCP server: validateJob(jobName="selfcheck", type="alphafold", settings={"sequence": "MKTAYIAKQRQISFVKSHFSRQLEERLGLIE"}) → valid: true.

Validated input payloads

AlphaFold — monomer

json

{ "sequence": "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKR",
  "numModels": "1", "numRecycles": 3 }

Only sequence is required; everything else has a default. numModels is a string dropdown ("1"–"5").

AlphaFold — multimer (colon-separated chains)

json

{ "sequence": "MKTAYIAKQRQISFVKSHFSRQLEERLGLIE:DIQMTQSPSSLSASVGDRVTITCRASQSISSYLN" }

Join chains with :. No separate "multimer" flag — chain count drives it.

Boltz-2 — sequence mode

json

{ "inputFormat": "sequence",
  "sequence": "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALP" }

inputFormat is required ("sequence" / "list" / "molecules" / "yaml"). Omitting it fails — see "What fails" below.

DiffDock — protein + SMILES ligand

json

{ "ligandFormat": "SMILES",
  "ligandSmiles": "CC(=O)Oc1ccccc1C(=O)O",
  "proteinFile": "<uploaded-path-or-inline-PDB-text>" }

ligandFormat chooses the conditional field: "SMILES" → ligandSmiles; "sdf/mol2 file" → ligandFile. proteinFile is a file param — pass an uploaded file's bare filename (target.pdb, not email-prefixed), a prior-job path (JobName/...), or inline PDB text (see file-input rules in api_reference.md).

Autodock Vina — protein + SMILES ligand (classical docking into a pocket)

json

{ "receptorFile": "receptor.pdb",
  "ligandFormat": "smiles",
  "ligandSmiles": "CC(=O)Oc1ccccc1C(=O)O",
  "boxX": 15.19, "boxY": 53.903, "boxZ": 16.917,
  "width": 20, "height": 20, "depth": 20 }

Unlike DiffDock, Autodock Vina docks into a fixed pocket, so it requires a bounding box (boxX/Y/Z center + width/height/depth, all required) and the receptor in receptorFile (not proteinFile). Its ligandFormat enum is lowercase ("smiles" / "sdf") — different from DiffDock's "SMILES" / "sdf/mol2 file", so don't copy DiffDock's value across. exhaustiveness (default 8) is optional. validateJob-confirmed.

ProteinMPNN — design residues on a backbone

json

{ "pdbFile": "<uploaded-path-or-inline-PDB-text>",
  "designedResidues": { "A": "1 2 3 4 5" },
  "numSequences": 4, "modelType": "proteinmpnn" }

Requires pdbFile + designedResidues (per-chain, space-separated resnums). modelType ∈ proteinmpnn/ligandmpnn/solublempnn/hypermpnn/abmpnn. Note designedChains is exclude:["api"] — don't send it over the API.

Batch (same tool, many jobs)

json

{ "batchName": "screen-1", "type": "alphafold",
  "jobNames": ["s1", "s2"],
  "settings": [ { "sequence": "MKT..." }, { "sequence": "AVF..." } ] }

What fails (and the exact error) — confirmed live

Boltz without inputFormat → valid:false, Missing required boltz field "inputFormat". Always check required fields with getJobSchema first; sequence alone is not enough for boltz/chai.
Building a submit from validateJob's normalized blob — normalized is informational (defaults filled in, sometimes platform-managed fields). Submit the clean settings you validated, not the normalized echo.
File param given a bare string that isn't a real path → treated as INLINE file content (uploaded as <email>/<jobname>-<param>.<ext>), not a reference. To point at an existing uploaded file use its bare filename (target.pdb — do NOT email-prefix it; {email}/{filename} is the S3 key and 400s as not-uploaded), or JobName/... for a prior job's output. Referencing a path that doesn't exist → File ... has not been uploaded.

Output shapes (describe, don't expect exact values)

Outputs are non-deterministic (seed/model/MSA) — reason about the shape, not golden numbers.

Job row Score (JSON string on completed jobs): tool-family dependent.
- Folding (alphafold/boltz/chai/esmfold): plddt, ptm, and for complexes iptm plus interface metrics (ipSAE_*, pDockQ_*). Higher pLDDT/pTM = more confident; iptm/ipSAE gauge interface quality.
- Other families carry their own metrics — read the keys, don't assume.
Results zip (POST /result → presigned URL → GET): per-tool, typically the structure files (rank_*.pdb / *.cif), a scores CSV, and logs. Use listJobFiles(jobName) (MCP) to enumerate exact filenames before downloading.
WeightedHours on the row is the billing unit (see usage-statistics).

To learn a specific tool's exact outputs, run one small job and listJobFiles it — don't hardcode filenames, which vary by tool and version.