/tamarind

name: tamarind
description: Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.
license: MIT
compatibility: Requires Python 3.10+, a Tamarind Bio account, and an API key from app.tamarind.bio. Uses the `requests` library against the public REST API (these recipes use HTTP directly). Network access required. Optional MCP server at mcp.tamarind.bio/mcp for agent hosts.
metadata:
  version: "1.3"
  last-reviewed: "2026-09-30"
  skill-author: Tamarind Bio
  trigger-keywords: protein structure prediction, AlphaFold, Boltz, Chai, ESMFold, protein design, binder design, de novo design, antibody design, nanobody, protein-ligand docking, DiffDock, Autodock Vina, binding affinity, MSA generation, inverse folding, ProteinMPNN, RFdiffusion, BoltzGen, cloud GPU biology, structure prediction API, x-api-key, developability, adme, enzyme, peptide, protein language models, molecular design
  openclaw:
    primaryEnv: TAMARIND_API_KEY
    envVars:
    - name: TAMARIND_API_KEY
      required: true
      description: Tamarind Bio API key sent as the x-api-key header.

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](https://app.tamarind.bio/llms.txt) and

[complete guide](https://app.tamarind.bio/llms-full.txt).

• [Current OpenAPI](https://app.tamarind.bio/api/openapi.json) includes discovery,

validation, jobs, files, and newer pipeline/custom-tool surfaces. [openapi.yaml](https://app.tamarind.bio/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](https://app.tamarind.bio/tools.json) 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](https://docs.tamarind.bio/llms.txt) links to

Markdown pages, including the [MCP guide](https://docs.tamarind.bio/tamarind/mcp-server.md).

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](https://app.tamarind.bio/api-docs/custom-tools-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`. Persi