/tamarind

name: tamarind
description: Access 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 (no dedicated Python SDK exists). Network access required. Optional MCP server at mcp.tamarind.bio/mcp for agent hosts.
metadata:
  version: "1.1"
  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 Bio is a cloud platform that runs computational biology tools — structure prediction, protein and antibody design, docking, binding-affinity, MSA generation, and molecular dynamics — on managed GPUs. Users submit sequences or structures and get back predicted structures, designs, and biophysical scores, without provisioning their own hardware. It exposes hundreds of tools (AlphaFold, Boltz-2, Chai-1, RFdiffusion, ProteinMPNN, BoltzGen, ESMFold2, DiffDock, Autodock Vina, and many more) through one uniform job API.

**Official docs:** [app.tamarind.bio/api-docs](https://app.tamarind.bio/api-docs) · platform UI at [app.tamarind.bio](https://app.tamarind.bio)

Canonical sources — fetch these, don't rely on a stale copy

Tamarind publishes live, machine-readable sources. Prefer fetching them at runtime over trusting any hardcoded list — tool names, schemas, and endpoints change frequently:

• **`https://app.tamarind.bio/llms.txt`** — LLM index: links to the spec, API docs, and MCP guide.
• **`https://app.tamarind.bio/openapi.yaml`** — OpenAPI 3.0 spec for the 8 core job endpoints (submit-job/-batch, jobs, result, upload, files, delete-job/-file; auth `ApiKeyAuth`). Fetch it for those exact shapes. Discovery/management endpoints (`/tools`, `/usage-statistics`, pipelines, …) aren't in it — use the MCP/REST discovery tools for those.
• **`https://docs.tamarind.bio/llms.txt`** — documentation index; every page has a `.md` form (e.g. `docs.tamarind.bio/tamarind/batch.md`, `/tamarind/api.md`, `/tamarind/pipelines.md`).
• **Live tool discovery** — `GET /tools` (REST) or MCP `getAvailableTools` + `getJobSchema(jobType)` are the source of truth for what tools exist and their parameters.

This skill teaches the surface + the non-obvious behaviors those sources don't spell out (see the reference files). When in doubt about a shape, fetch `openapi.yaml`.

When to use this skill

Use Tamarind when the user wants to:

• **Predict structure** of a protein, complex, or protein-ligand system (AlphaFold, Boltz-2, Chai-1, ESMFold2, Chai/Boltz cofolding)
• **Design proteins or binders** (RFdiffusion, BoltzGen, BindCraft, ProteinMPNN/LigandMPNN inverse folding)
• **Design or characterize antibodies/nanobodies** (sequence generation, humanization, developability, immunogenicity)
• **Dock small molecules** to a protein (DiffDock, Autodock Vina) or predict **binding affinity**
• **Generate MSAs** for downstream folding
• **Run molecular dynamics** or other biophysical workflows on managed GPUs
• **Batch-screen** many sequences or designs through the same tool
• **Chain tools** into pipelines (e.g. design → fold → score) using the output of one job as the input of the next

This skill is the right fit when the work should run on Tamarind's managed cloud rather than on a local install. For purely local cheminformatics or one-off sequence I/O, use a local library (RDKit, BioPython) instead.

Access and authentication

1. Sign in at [app.tamarind.bio](https://app.tamarind.bio) and create an API key from the account/API settings. 2. Authenticate every REST request with the `x-api-key` header. 3. **Never hardcode the key.** Read it from the `TAMARIND_API_KEY` environment variable or a `.env` file (use `python-dotenv`). Never commit keys to source control.

**Pricing:** Every user gets **10 free jobs**. For larger usage, contact [info@tamarind.bio](mailto:info@tamarind.bio) to purchase a subscription.

export TAMARIND_API_KEY="your_api_key"
# List available tools
curl https://app.tamarind.bio/api/tools \
  -H "x-api-key: $TAMARIND_API_KEY"

**Base URL:** `https://app.tamarind.bio/api/`

There is **no official Python SDK** — the PyPI package named `tamarind` is an unrelated Neo4j tool. Do not `uv pip install tamarind`. Write plain `requests` calls against the REST API (the endpoint shapes are in `openapi.yaml`), or use the MCP server for agent hosts.

Two ways to call Tamarind

MCP server (best for AI agents)

Tamarind hosts an MCP server at `https://mcp.tamarind.bio/mcp` (API-key auth via the `X-API-Key` header). When your agent host supports MCP, prefer it — the tools mirror the REST API with agent-friendly schemas:

• `listModalities()` / `lis