evo2-mcp-server
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- AlicenseAqualityCmaintenanceEnables genomic sequence analysis through the Evo 2 model, supporting DNA sequence scoring, embedding, generation, and variant effect prediction with multiple model checkpoints (7B, 40B, 1B parameters).62LGPL 3.0

bio-mcp-evo2official
AlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI assistants to generate, score, and analyze DNA sequences using the evo2 genomic foundation model. It supports multiple execution modes including local GPU, SLURM clusters, and the Nvidia NIM cloud API for tasks like variant effect prediction and sequence embedding.1MIT- AlicenseAqualityBmaintenanceMCP server offering verified bioinformatics tools for sequence utilities and statistics, backed by BioPython/scipy. Enables AI agents to perform accurate GC content, translation, ORF finding, motif scanning, and statistical tests through natural language.11MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to query clinical genomics databases, retrieve supporting literature, analyze population genetics, and visualize biological pathways.18MIT
- FlicenseNot gradedqualityDmaintenanceEnables protein sequence analysis and structure prediction by extracting ESM-2 embeddings and batch processing FASTA files via Docker. It provides tools for large-scale embedding extraction, job monitoring, and model management within an MCP-compatible environment.-
- AlicenseBqualityDmaintenanceEnables AI-powered genomic variant analysis including variant impact prediction, regulatory element discovery, and batch variant scoring. Currently operates in mock mode as a proof-of-concept awaiting the public release of Google DeepMind's AlphaGenome API.209 npm2MIT
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: forward pass for layer tensors, sequence log-likelihood scoring, single variant effect, batch variant scoring, and FASTA-based scoring. The descriptions clearly delineate usage scenarios, reducing misselection risk.
All tools follow the evo2_ verb-noun pattern (forward, score, variant_score, batch_score, score_fasta). While the second part varies, the consistent prefix and action-oriented naming make the set predictable and easy to navigate.
Five tools is well-scoped for a focused DNA model inference server. Each tool adds a distinct capability without redundancy, covering single sequence, variant, batch, and file-based scoring.
The surface covers the core workflows for an Evo2 model: sequence scoring, variant effect analysis (single and batch), FASTA batch processing, and forward pass tensor extraction. No obvious dead ends or missing critical operations for the stated purpose.