tamarind

# 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. ```bash 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()` / `listTags()` — the live filter vocabulary (molecule type / function) with labels + tool counts; call these to learn valid `modality`/`function` values instead of hardcoding - `getAvailableTools(modality?, function?, search?, custom?)` — discover tools (`category`/`tag` are deprecated aliases still honored) - `getJobSchema(jobType)` — exact parameter schema for a tool, plus an `exampleJob` starting payload (validate it before submitting) - `validateJob(jobName, type, settings)` — dry-run validation before submitting - `submitJob(jobName, type, settings)` / `submitBatch(batchName, type, settings[], jobNames[])` - `getJobs(jobName?, batch?, limit?, includeSequences?)` — list/inspect jobs and statuses (the bulky per-job input blob is omitted by default; pass `includeSequences=true` to keep it) - `getJobLogs(jobName)` — fetch output logs for debugging - `listJobFiles(jobName)` — list output files (returns `s3Path` for chaining) - `getResult(jobName, fileName?)` — download results - `uploadFile(filename)` — presigned upload URL; or `uploadFileContent(filename, content, encoding?)` to send file content through MCP when the host can't reach S3 (sandboxed agents) Scope note: MCP query tools (`getJobs`, `getResult`, `listJobFiles`, …) are scoped to the authenticated account. ### REST API (universal) Use plain HTTP with `requests` — the endpoint shapes are in `openapi.yaml`. The core loop is below; `references/workflows.md` has full recipes. ## Core workflow Always follow discover → schema → validate → submit → poll → results. Do not hardcode tool names or settings — the catalog changes frequently. ```python import os, time, requests BASE = "https://app.tamarind.bio/api" HEADERS = {"x-api-key": os.environ["TAMARIND_API_KEY"]} # 1. Discover tools. REST /tools returns the full list; filter client-side. tools = requests.get(f"{BASE}/tools", headers=HEADERS).json() alphafold = next(t for t in tools if t["name"] == "alphafold") # 2. Get the exact schema for the chosen tool. # REST: each /tools entry already includes its inline `settings` schema # (parameter list) — find the entry whose name == your job type. # MCP: getJobSchema(jobType) returns the same per-tool detail. # 3. Submit a job. `settings` is tool-specific — match the schema exactly. payload = { "jobName": "my-alphafold-run", # ^[a-zA-Z0-9_-]+$, <=100 chars, unique "type": "alphafold", "settings": { "sequence": "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG", "numRecycles": 3, }, } resp = requests.post(f"{BASE}/submit-job", headers=HEADERS, json=payload) resp.raise_for_status() # 200 ok; 400 bad request; 403 budget exceeded; 401 unauthorized # 4. Poll for completion. # NOTE the response shape: GET /jobs?jobName=<name> returns the job ROW # directly (no "jobs" wrapper); the list query (no jobName) returns # {"jobs": [...]}. Don't index ["jobs"][0] on the by-name response. while True: job = requests.get(f"{BASE}/jobs", headers=HEADERS, params={"jobName": "my-alphafold-run"}).json() if job["JobStatus"] in ("Complete", "Stopped", "Deleted"): break time.sleep(30) # 5. Retrieve results. POST /result returns a presigned URL *string*; # GET that URL to download the actual results zip (two-step). url = requests.post(f"{BASE}/result", headers=HEADERS, json={"jobName": "my-alphafold-run"}).text.strip('"') open("my-alphafold-run.zip", "wb").write(requests.get(url).content) ``` For the agentic version of this loop using MCP tools, and for richer examples, see `references/workflows.md`. ## Discovering tools The catalog has hundreds of tools. Always enumerate at runtime — never rely on a hardcoded list. **REST** `GET /tools` returns the **full list** (it does not filter server-side); each item is `{name, displayName, github, paper, description, settings}` where `settings` is that tool's inline parameter schema. Filter client-side: ```python tools = requests.get(f"{BASE}/tools", headers=HEADERS).json() # a list boltz = [t for t in tools if "boltz" in t["name"].lower()] ``` Note: both surfaces return one row per tool name — REST `/tools` and MCP `getAvailableTools` are both deduplicated (the MCP keeps the newest tool version), so a name match returns a single row. **MCP** `getAvailableTools(search=..., modality=..., function=...)` filters server-side and adds `categories`/`tags` per tool (`category`/`tag` are deprecated aliases of `modality`/`function`, still honored). Don't hardcode the vocabulary — it drifts. Get the live values from `listModalities()` / `listTags()` (each returns `value`, `label`, `description`, and `toolCount`), or read the `availableCategories` / `availableTags` facet arrays returned on every `getAvailableTools` response. Modalities are molecule types (protein, antibody, peptide, small-molecule, nucleic-acid, …); functions are what a tool does (structure-prediction, binder-design, protein-ligand-docking, …). A representative set of widely-used tools (verify with `/tools`): `alphafold`, `boltz` (Boltz-2), `chai` (Chai-1), `esmfold` / `esmfold2`, `rfdiffusion`, `proteinmpnn`, `ligandmpnn`, `boltzgen`, `bindcraft`, `diffdock`. See `references/tool_catalog.md` for the full category/tag map and how to read tool metadata. ## Choosing the right tool The catalog has many tools per task; **don't hardcode a favorite — filter by `function` (and `modality`), then read each candidate's `description` and match it to the user's actual goal** (input you have, output you need, constraints like speed or "no MSA"). The `description` and `tags` fields are the public "what it's for" signal; let them, plus `validateJob`, drive the pick. Quick orientation by task: - **Fold a single protein / complex** (`function=structure-prediction`): the AlphaFold3-class reproductions — `boltz`/`chai`/`openfold`/`protenix`/`intfold` — are the accurate default for **everything**, including protein-only systems; they also handle **nucleic-acid + small-molecule complexes**, so reach for them whenever a ligand/RNA/DNA is part of the system (and `boltz` adds binding-affinity). `alphafold` (AF2) remains a solid choice for monomers + multimers (join chains with `:`). `esmfold` is single-sequence (no MSA) and fast — reach for it when you want speed and have no MSA; `esmfold2` is newer and conditions on an MSA by default (its `model` setting offers a faster single-sequence mode). Specialized folders exist for antibodies (`abodybuilder`, `immunebuilder`), cyclic peptides (`highfold`), and conformational ensembles (`afcluster`, `alphaflow`) — filter and read descriptions. - **Design a binder** (`function=binder-design`): `bindcraft` (de novo miniprotein binders) and `boltzgen` (binders for protein **and** small-molecule targets, incl. nanobodies/antibodies/peptides) are the go-to de novo binder tools; `rfdiffusion` also does binder design and is the pick for **motif scaffolding** / diversifying an existing backbone. Antibody-specific generators live under `function=antibody-design`. - **Design sequence for a known backbone** (`function=inverse-folding`): `proteinmpnn` (general), `ligandmpnn` (ligand-aware), plus thermostable/soluble/antibody MPNN variants. Inverse folding takes a **structure** and emits **sequences** — fold them back to verify (see chaining). - **Dock a small molecule** (`function=protein-ligand-docking`): prefer `boltz`/`chai` — they co-fold the ligand into the complex and predict the bound structure rather than docking into a fixed receptor; reach for `autodock-vina` when you need fast, large-scale screening against a known pocket. - **Predict binding affinity** (`function=binding-affinity`) or **generate an MSA** (search `msa`) — filter and read. When the user names a specific tool, evaluate that one **and** sanity-check the alternatives in its `tag` group — a faster or more appropriate sibling often exists. When unsure, `getJobSchema`/`validateJob` to confirm a candidate actually accepts the input you have before committing. ## Job settings, schemas, and validation Each tool has its own `settings` schema. Fetch it before submitting: - **REST** `/tools` entry: each `settings` param is a **trimmed** dict. Only `name` and `required` are always present; `type`, `default`, `description`, `options` appear only when relevant (≈60% have `type`) — so use `param.get("type")`, not `param["type"]`. The advanced gating keys (`exclude`, `conditionals`) are **NOT in the REST response** at all. - **MCP** `getJobSchema(jobType)`: the **full** schema, including `exclude`, `conditionals`, and bounds. Use MCP when you need to reason about those gating keys. (`restrictOrgs` is stripped on both surfaces — an org-gated param you can't use is simply omitted; see `references/api_reference.md`.) **Always `validateJob` (MCP) before submitting** — it's the reliable guard. It runs the same validation as `/submit-job` without submitting, and surfaces the first missing/invalid field. Don't try to hand-derive which fields to strip from the schema keys (over REST you can't see them anyway) — let `validateJob` tell you. (The response may include a `source` field, e.g. `"static-fallback"` — an internal note on which schema source validated; `valid: true/false` is the signal you act on.) `validateJob` echoes a `normalized` view of your settings with defaults filled in. Submit the same clean `settings` you validated; treat `normalized` as informational (it can carry defaults you didn't set, and for some tools platform-managed fields), so build your submit from your own settings rather than the normalized blob. **Sequences:** amino-acid string; separate chains of a multimer with a colon (`:`), e.g. `"MVLS...:EVQL..."`. Note that some tools (e.g. `boltz`, `chai`) require more than `sequence` — `boltz` also requires `inputFormat` (and accepts `yamlFile`/`molecules`). Always `getJobSchema`/`validateJob` to learn a tool's required fields; don't assume `sequence` alone suffices. **Platform-internal fields** — never set these yourself; the platform owns them: `submit_method`, `monomer_msa`, `msa`. See `references/api_reference.md` for the full field-handling rules. **Surface consequential choices before submitting, don't default silently.** When the request fully specifies what to run, proceed. But when it's open-ended, or when a setting materially changes the results, runtime, or cost (model/variant, number of samples or seeds, MSA on/off, GPU tier, batch size), present the meaningful options plus the default you'd otherwise apply and let the user pick **before** you submit — rather than choosing silently and reporting it after the job is queued. `getJobSchema` and `validateJob`'s `normalized` show exactly which knobs you're filling in on the user's behalf, so you can flag the few worth a quick confirm. This matters most for **batches**, where one shared-settings choice multiplies across every job. ## File inputs (PDB, CIF, SDF, …) Tools with file parameters accept input three ways: