Chai-1 MCP Server

Protein structure prediction using the Chai-1 model via Docker

An MCP (Model Context Protocol) server for Chai-1 structure prediction with 6 core tools:

• Predict structures for small peptides (synchronous, instant results)
• Submit basic structure predictions from FASTA sequences
• Submit MSA-enhanced predictions for improved accuracy
• Batch process multiple FASTA files
• Monitor and retrieve job results
• Validate FASTA files before submission

Quick Start with Docker

Approach 1: Pull Pre-built Image from GitHub

The fastest way to get started. A pre-built Docker image is automatically published to GitHub Container Registry on every release.

# Pull the latest image
docker pull ghcr.io/macromnex/chai1_mcp:latest

# Register with Claude Code (runs as current user to avoid permission issues)
claude mcp add chai1 -- docker run -i --rm --user `id -u`:`id -g` --gpus all --ipc=host -v `pwd`:`pwd` ghcr.io/macromnex/chai1_mcp:latest

Note: Run from your project directory. ` pwd ` expands to the current working directory.

Requirements:

• Docker with GPU support (nvidia-docker or Docker with NVIDIA runtime)
• Claude Code installed

That's it! The Chai-1 MCP server is now available in Claude Code.

Approach 2: Build Docker Image Locally

Build the image yourself and install it into Claude Code. Useful for customization or offline environments.

# Clone the repository
git clone https://github.com/MacromNex/chai1_mcp.git
cd chai1_mcp

# Build the Docker image
docker build -t chai1_mcp:latest .

# Register with Claude Code (runs as current user to avoid permission issues)
claude mcp add chai1 -- docker run -i --rm --user `id -u`:`id -g` --gpus all --ipc=host -v `pwd`:`pwd` chai1_mcp:latest

Note: Run from your project directory. ` pwd ` expands to the current working directory.

Requirements:

• Docker with GPU support
• Claude Code installed
• Git (to clone the repository)

About the Docker Flags:

• -i — Interactive mode for Claude Code
• --rm — Automatically remove container after exit
• ` --user id -u:id -g ` — Runs the container as your current user, so output files are owned by you (not root)
• --gpus all — Grants access to all available GPUs
• --ipc=host — Uses host IPC namespace for better performance
• -v — Mounts your project directory so the container can access your data

Verify Installation

After adding the MCP server, you can verify it's working:

# List registered MCP servers
claude mcp list

# You should see 'chai1' in the output

In Claude Code, you can now use all 6 Chai-1 tools:

• predict_small_peptide
• submit_basic_prediction
• submit_msa_prediction
• submit_batch_prediction
• get_job_status
• get_job_result

Next Steps

• Detailed documentation: See detail.md for comprehensive guides on:
• Available MCP tools and parameters
• Local Python environment setup (alternative to Docker)
• Example workflows and use cases
• MSA server configuration
• Configuration file format

Usage Examples

Once registered, you can use the Chai-1 tools directly in Claude Code. Here are some common workflows:

Example 1: Quick Peptide Prediction

I have a short peptide sequence "GAAKLKKTFR". Can you predict its structure using predict_small_peptide and save the result to /path/to/output/?

Example 2: Full Protein Structure Prediction

I have a protein FASTA file at /path/to/protein.fasta. Can you submit a basic structure prediction using submit_basic_prediction with output saved to /path/to/results/, then monitor the job until it completes and retrieve the final structure?

Example 3: MSA-Enhanced Prediction

I want high-accuracy structure prediction for my protein at /path/to/protein.fasta. Can you use submit_msa_prediction with use_msa_server set to True to include evolutionary information? Save results to /path/to/msa_results/.

Troubleshooting

Docker not found?

docker --version  # Install Docker if missing

GPU not accessible?

• Ensure NVIDIA Docker runtime is installed
• Check with docker run --gpus all ubuntu nvidia-smi

Claude Code not found?

# Install Claude Code
npm install -g @anthropic-ai/claude-code

License

Based on chai-lab by Chai Discovery