Protein Design MCP Server

An MCP server that gives an LLM agent 41 atomistic protein-design tools: 39 run_* tools that each run exactly one engine, plus describe_tool and get_job_status.

Every step is a separate call. Generation, sequence design, folding, alignment and scoring are never bundled, so the agent chooses each one and can be asked to defend the choice.

v2 is a breaking change — read this first

If you are looking for design_binder, predict_complex, predict_structure, analyze_interface or score_stability, you want v1.0.0 (image jeonghyeonkim8652/protein-design-mcp:1.0.0, still published and untouched).

v2 removes all five. They were composites: one call that ran several engines behind a fixed pipeline. Two things were wrong with that.

1. They hid the decisions. design_binder picked the generator, the sequence designer and the structure predictor for you, so an agent asking for a binder got one opinion with no way to argue with it.

2. design_binder was wrong. It assumed a chain order that does not hold, and returned the target chain as its own design. The bug is invisible from the outside — the output is a valid PDB of a real protein — and it is why this rewrite exists.

The replacement is not a like-for-like tool. It is the same work, written out:

run_interface_residues | run_epitope_scan        ->  hotspots
  -> run_rfdiffusion3_binder | run_boltzgen_design | run_genie3_binder | ...
  -> run_mpnn | run_boltzgen_inverse_fold
  -> run_esmfold2 | run_boltz | run_chai1 | ...   (MSA stated, never inherited)
  -> run_ipsae | run_prodigy | run_rosetta_interface

Related MCP server: multivon-mcp

The tools

Classified by function, not by which engine they came from — so an agent picking a binder generator sees seven interchangeable options rather than seven product names.

Engines include RFdiffusion 1/2/3, Genie 2/3, FrameFlow, MultiFlow, La-Proteina, Protpardelle-1c, Proteina-Complexa, BoltzGen, ProteinMPNN, ESMFold2, Boltz-2, Chai-1, Protenix v1, OpenFold3, Promera, RoseTTAFold3, AlphaFold 3, AlphaFold2-Multimer, MMseqs2, ColabFold, ipSAE, PRODIGY, PyRosetta and OpenMM.

docs/TOOLS.md is the full list, with every tool, its engine, and the design rules summarised below. Per-tool reference pages are in docs/tools/, one generated from each manifest.

Two rules worth knowing before you call anything

MSA is always supplied, never generated inside a folding tool. No folding tool builds its own alignment; it comes from the msa category or not at all. The msa parameter has no default — null means run MSA-free, a path means use that alignment, and omitting it is rejected. There is no "auto", because a folding tool that searches its own databases makes two models incomparable (the difference between their outputs confounds the model with the alignment), and because several engines default to a remote MSA server, which is how a novel design silently leaves the machine.

chains is never inferred. Whether a prediction runs with the target present or on the binder alone is the caller's decision, and the same binder predicted alone and in complex are different experiments.

Parameters

400 parameters across the 39 tools, mean 10.3 per tool. Every one has a description saying what it does, what changes when it moves, and a sensible range; 75% carry an enum, minimum/maximum or pattern. No engine flag is fixed outside the schema — what the manifests pin is plumbing only (PYTHONPATH, cache locations, CUDA_HOME), never a scientific choice.

Running the 2.4.5 integrated image

The repaired release is published on Docker Hub. Its verified immutable reference is:

jasonkim8652/protein-design-mcp:2.4.5@sha256:b6b81defafb145881c5f21eb3e5937d93fb9f1878afa7d9b5b6a261327219e91

The image contains server revision f3d41f1. See the 2.4.5 publication and runtime validation record.

The image contains isolated engine environments, engine code, CUDA toolkit components, and redistributable public model weights. Engine executables and editable source mappings use fixed in-image paths; no developer home or host conda environment is required. The all-engine image has 181.41 GiB of compressed registry layers and is 265.78 GiB uncompressed, before run outputs and Docker's additional storage requirements.

The packaged runtime versions are listed in docs/integrated-environments.json; public asset locations and upstream license references are recorded in docs/integrated-assets.json.

mkdir -p "$PWD/workspace"
docker run -i --rm --device=nvidia.com/gpu=0 --shm-size=16g \
  --user "$(id -u):$(id -g)" -e HOME=/tmp \
  -e TMPDIR="$PWD/workspace" -v "$PWD/workspace:$PWD/workspace" \
  jasonkim8652/protein-design-mcp:2.4.5

This starts the MCP stdio server. Keep the input/output workspace mounted at its identical absolute path so returned artifact paths are readable by the client. Choose an allocated GPU and provide a compatible NVIDIA host driver and Docker GPU support.

External assets

Only external databases and user-obtained licensed materials need additional read-only mounts. The host folders can have arbitrary names and locations.

For example, add --mount type=bind,source=/your/af3-weights,target=/data/models/alphafold3,readonly before the image name. An empty directory is not a completed database or weight installation. All parent directories must allow traversal and files must be readable by the invoking account. The image imports only the licensed PyRosetta packages from its mount, preserving the bundled dependencies.

Missing external assets exclude dependent tools with a startup explanation. ColabFold remote search remains available without its optional local DB; local mode checks its selected database files before launching. AF2-Multimer includes all five multimer_v3 parameter sets and needs none of these external assets when passed msa: null or a supplied A3M.

ProteinMEM provides an asset-path JSON template, a config generator, and scripts/check_runtime_paths.py --require-all to inspect paths and discover all 41 tools before a campaign. Discovery verifies availability declarations; actual inference and database searches require separate smoke tests.

OpenMM adds missing terminal atoms such as OXT before hydrogens, and reports added_terminal_atoms. Its energies are force-field potential energies; E_complex - E_binder - E_target is a computational proxy, not a measured binding free energy.

For campaign archival, set PROTEIN_MCP_KEEP_WORKDIR=1 and place TMPDIR on a writable workspace mount. Calls then retain all engine intermediates and full engine.stdout.log / engine.stderr.log files, including partial output on failure or timeout. Tool responses expose their paths in execution_artifacts; callers can copy them into a campaign archive. Without this option, successful scratch directories are removed after declared outputs are collected. Retained work directories consume additional disk space.

Building the image

For the 2.4.5 source-only runtime patch, reuse the immutable published base:

docker build -f Dockerfile.runtime-patch \
  --build-arg SOURCE_REVISION="$(git rev-parse HEAD)" \
  -t jasonkim8652/protein-design-mcp:2.4.5 .

The following commands describe the integrated 2.4.0 base build.

Dockerfile.envs builds the core environments. Dockerfile.integrated adds curated and relocated engine environments, source and public weights:

docker build -f Dockerfile.envs -t protein-design-mcp:2.4.0-core .
# After staging and auditing all engine environments and public assets:
python scripts/assemble_integrated_payload.py \
  --rootfs /your/staged/rootfs --output /your/prepared-payload/rootfs.tar
python scripts/plan_integrated_layers.py \
  --archive /your/prepared-payload/rootfs.tar \
  --output /your/build/Dockerfile.integrated.layered
docker build -f /your/build/Dockerfile.integrated.layered \
  --build-context payload=/your/prepared-payload \
  -t jasonkim8652/protein-design-mcp:2.4.0 .

The layer planner keeps complete files and TAR member order while targeting 4 GiB of payload per layer. Larger individual files remain intact. This avoids uploading all environments and weights as one large registry blob. The source archive is read directly; no second payload archive is created. Docker storage drivers may copy hard-linked files from earlier layers, so actual layer sizes can exceed the payload target and deduplication may be reduced. Verify the built image's layer sizes before publication.

The staging helpers scripts/prepare_integrated_envs.py and scripts/prepare_integrated_assets.py accept explicit machine-local input inventories. They copy installed runtimes without changing source files, relocate prefixes and editable installs, and allowlist public assets. Restricted weights, PyRosetta distributions, credentials, unrelated caches and user databases must be excluded before creating the archive; deleting them in a later Docker layer would still distribute them. Preserve component licenses and /opt/models/licenses/upstream/ASSET-MANIFEST.json with the payload. AF3's bundled, pinned source revision retains its own CC BY-NC-SA license; the server's Apache license does not replace component licenses.

Dockerfile, Dockerfile.full, Dockerfile.lite, Dockerfile.colabfold and Dockerfile.patch are historical v1 recipes, not the integrated release.

Adding a tool

A tool is one YAML manifest. No Python.

src/protein_design_mcp/manifests/run_<name>.yaml

The manifest is the single source for the MCP Tool (name, summary, inputSchema), the describe_tool response, the dispatch entry, and the generated page in docs/tools/. After editing one:

python scripts/generate_tool_docs.py     # tests/test_doc_generation.py fails if you forget
pytest tests/ -q

Development

pip install -e ".[dev]"
pytest tests/ -q

Tests that need a GPU engine's host environment skip when it is absent. The suite also derives and checks deployment facts — mount completeness, the container command's shape, doc freshness — because those are the defects unit tests cannot see.

License

Apache 2.0 for this server (see LICENSE). The engines it dispatches to carry their own licenses, several of which are non-commercial or require a separate grant; AlphaFold 3 weights and PyRosetta are mounted from the host rather than distributed here for exactly that reason. Check each engine's terms before use.

References

• MCP Specification

• docs/TOOLS.md — the full tool list and the rules behind it

• docs/tools/ — generated reference page per tool