MCP server for generating, scoring and embedding genomic sequences using Evo 2
MCP server for generating, scoring and embedding genomic sequences using Evo 2. Exposed over MCP by the evo2 mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
The evo2-mcp server exposes Evo 2 as a Model Context Protocol (MCP) server, providing tools for genomic sequence analysis. Any MCP-compatible client can use these tools to score, embed, and generate DNA sequences.
The server ships on PyPI as flash-attn, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Everything the assistant can do here goes through one of these:
score_sequence — Evaluate DNA sequence likelihoodembed_sequence — Extract feature representationsgenerate_sequence — Generate novel DNA sequencesscore_snp — Predict variant effectsget_embedding_layers — List available embedding layerslist_available_checkpoints — Show supported model checkpointsThis sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Evo2 MCP's toolset — score_sequence, embed_sequence, generate_sequence and 3 more — is a fair guide to whether it matches your workflow.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| score_sequence | Evaluate DNA sequence likelihood |
| embed_sequence | Extract feature representations |
| generate_sequence | Generate novel DNA sequences |
| score_snp | Predict variant effects |
| get_embedding_layers | List available embedding layers |
| list_available_checkpoints | Show supported model checkpoints |
{
"mcpServers": {
"evo2-mcp": {
"command": "python",
"args": ["-m", "evo2_mcp.main"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.