Tamarind

Tamarind Bio

Tamarind runs molecular-design and structural-biology tools on managed compute: structure prediction, protein and antibody design, docking, binding-affinity prediction, MSA generation, and molecular dynamics. Use it when the user requests Tamarind, its REST API/MCP server, or cloud execution of these scientific tools. For local sequence processing or molecular descriptors, use a local library.

Sources and review scope

The REST contracts and public catalog were reviewed on 2026-09-30. Examples are illustrative until validated against the user's account; this review did not run authenticated jobs or establish scientific accuracy for any model.

• API index and complete guide.
• Current OpenAPI includes discovery, validation, jobs, files, and newer pipeline/custom-tool surfaces. openapi.yaml is also available. Check that needed paths exist: the merged spec can return HTTP 200 with only its classic surface when the backend spec cannot be fetched.
• Public catalog needs no key; query ?type= or ?tag=. It documents public tools and conditional required settings, not every optional parameter or account entitlement.
• Product documentation index links to Markdown pages, including the MCP guide.

Fetch the account's current schemas before composing a run. Where the prose guide and OpenAPI differ, prefer the operation/schema for field shapes, and record any unresolved difference rather than guessing.

Access

1. Use the user's Tamarind deployment. The shared base is https://app.tamarind.bio/api; a dedicated organization deployment has its own host and account data. Every relative REST path below is under /api.
2. Obtain a key from the deployment's API settings and read it from TAMARIND_API_KEY; send it as x-api-key. Keep keys out of files and logs.
3. Check the account's current allowance and compute budget before scaling up. Free usage is a monthly allowance, not an unconditional promise of ten jobs forever; billing and entitlements can change.

# Public discovery requires no credential.
curl --fail-with-body 'https://app.tamarind.bio/tools.json?type=alphafold'
# Account-scoped discovery:
curl --fail-with-body 'https://app.tamarind.bio/api/tools' \
  -H "x-api-key: $TAMARIND_API_KEY"

For REST examples install requests in the execution environment. The official CLI distribution is tamarind-cli, and its Custom Tools Python client imports as from tamarind import Tamarind; the unrelated package named tamarind is not this client. See the SDK reference. Core job recipes below use HTTP directly.

Workflow

1. Discover. Read GET /tools and match the user's scientific task to the tool description. Built-ins return an array; ?custom=true lists legacy custom tools only. Current custom deployments can be missing from this list: use the known deployed name and its schema before concluding that it is unavailable.
2. Read the schema. GET /tools/{name}/schema returns a JSON Schema for the settings object. GET /tools also supplies a trimmed settings parameter list. Check task-dependent fields, file extensions, list values, and defaults.
3. Validate. Send POST /validate-job with type, settings, and optional jobName. Check HTTP status first, then JSON valid. On success, inspect and use normalized as the settings to submit. Address unrecognized_settings if returned, even alongside valid: true: an optional-field typo can otherwise silently leave the default in effect. Validation checks fields, not all submit policies, queue limits, or deployment readiness.
4. Submit once. POST /submit-job takes jobName, type, settings, optional version for a custom-tool build, and optional/organization-required projectTag. Persist the submitted name and settings. A successful response is plain text, not a JSON receipt. Use the returned stored name.
5. Poll. GET /jobs?jobName=... returns a row directly. Single-job terminal states are Complete, Stopped, and Failed; handle legacy Deleted or an exact-lookup error without looping indefinitely. Poll batch parents using batchStatus, and poll newer pipelines on their own run endpoint.
6. Download and inspect. POST /result returns a JSON string URL on 200, or 202 with status: "preparing". Retry result retrieval after 202, without resubmitting compute. GET the signed URL without the Tamarind API-key header. Download the archive only for successful runs; request fileName: "output.log" for stopped/failed jobs. Verify the scientific outputs after downloading.

Workflow recipes implement validation, stored names, bounded polling, 202 handling, batch validation, and pagination. They are locally smoke-tested with simulated responses; authenticated execution remains untested.

Picking tools and interpreting results

Select by inputs, intended output, and modeling assumptions, then confirm the candidate in the live catalog. These are anchors, not a guaranteed catalog:

Confidence scores describe model confidence, not experimental binding, specificity, or affinity. Compare designed backbones, interfaces, clashes, chain/residue mapping, and developability. Check ligand chemistry and stereochemistry; docking scores are not interchangeable with measured binding free energies. Record tool/model, input provenance, chain mapping, seeds/samples, MSA/template choices, normalized settings, and any user-selected filtering thresholds.

Honor the user's selected tool and budget. Use authorized defaults for routine choices; surface unresolved choices that materially affect the scientific task or compute scope before a large campaign. Never silently substitute a different scientific task because its inputs are easier to supply.