Protein Design MCP

Compose protein-design operations over MCP

Select atomic tools according to the scientific objective. Do not force every task through one pipeline, and do not treat a model score as experimental proof.

Discover the connector

Find the enabled connector with host.mcp.list(), then inspect it with host.mcp.tools(server). The connector can expose:

• generate_backbone
• design_sequence
• predict_structure
• predict_complex
• rosetta_score
• rosetta_relax
• rosetta_interface_score
• score_stability
• energy_minimize

Discovery shows that the server started. Before running a model, also verify its execution route, external environment, pinned revision, checkpoint, compute resources and required network-isolation mechanism.

Select compute before provisioning

Call host.accelerator_status() before any GPU-only operation. It probes the daemon's local GPUs first and then lists configured SSH GPU routes. These are different from a BYOC provider catalogue and from model-backend readiness.

When both a local route and one or more SSH routes are candidates, ask the user to choose local or ssh:<alias> before downloading, installing or launching anything. Do not silently prefer either route. When only one route exists, state the selected execution_target and record it in the bring-up evidence. An empty SSH registry is not evidence that the local machine has no GPU, and a missing Docker executable does not make a natively usable local GPU disappear.

Acquire checkpoints only when needed

Do not ask for, locate or download checkpoints during generic connector discovery. Only enter this flow after selecting an operation whose tool schema requires checkpoint_path. Before that call, inspect whether the user already supplied a path. If not, stop provisioning and ask whether they have an existing local checkpoint; request its path plus any known digest. Do not search for or download weights while that question is unanswered, and do not make them download a second copy merely because it is outside a conventional directory.

If the user says there is no local checkpoint, use the normal approved network and tool bring-up controls to download it. The framework does not maintain a closed list of allowed scientific sources: resolve the source selected for this run to an immutable version, prefer an upstream-published checksum, compute the downloaded file's SHA-256 independently, and retain source URL, size and digest. An observed digest with no independently trusted reference proves transfer identity, not that the file is the intended model; report that distinction.

After acquiring code, environment or weights, run a small real inference canary on the selected execution target. The canary must exercise the same adapter, backend revision and checkpoint that the formal call will use, produce the output types the adapter promises, and pass the adapter's parser and digest checks. Set run_mode="canary" on this attempt. Record failed bring-up attempts. Only after its result contains a verified bringup_admission may a new attempt use run_mode="formal". A formal attempt made too early ends in a durable failure, so retry it with a new attempt_id after admission rather than reusing the failed ID. Only after a canary reaches a verified terminal success may the backend be used in the user's formal work. Then retry the original scientific operation instead of ending the task with “backend not configured.” If permission, licensing, source integrity, disk capacity or the canary genuinely blocks bring-up, report that specific blocker and do not fabricate results.

Choose only the operations the task needs

Typical compositions include:

• target-conditioned binder design: generate_backbone → design_sequence → predict_structure and predict_complex → interface scoring;
• backbone sequence redesign: design_sequence → predict_structure → optional physical scoring;
• fixed-motif sequence design: design_sequence with explicit per-chain fixed positions → structure validation;
• structure refinement: rosetta_relax or energy_minimize → score the input and refined structures with the same method;
• candidate ranking: combine sequence, monomer, complex and physical evidence while retaining the individual scores and provenance.

These are examples, not mandatory pipelines. Start from the user's design objective and constraints, then choose the smallest informative set of calls.

Record reproducible attempts

Give each model execution a stable attempt_id and explicit seed. Pin the backend revision and checkpoint SHA-256, use a dedicated output directory, and retain the resolved configuration, command, residue maps, raw outputs and terminal record. Reusing the same attempt and configuration is idempotent; changing the configuration under an existing attempt ID is a conflict.

One call to generate_backbone produces one design. Run multiple attempts with distinct IDs and seeds when sampling a population. A failed attempt remains part of the provenance rather than being silently discarded.