evo2-mcp-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| evo2_forwardA | Run a forward pass of Evo2-7B on a DNA sequence (forward inference) and return summary statistics — or raw tensors — for the requested model layers (final logits, attention, MLP or embedding outputs). Use this when you need layer outputs for analysis, not just a scalar score. Modes: 'summary' (shape/dtype/min/max/mean/std per layer — default, context-safe), 'save' (write the .npz under the server's output dir and return the path), 'raw' (inline small tensors only; large tensors must be saved to a file instead). This is a DNA foundation model inference tool. It does not provide clinical diagnosis. Model scores should not be interpreted as pathogenicity labels without additional validation. |
| evo2_scoreA | Compute the model-based log-likelihood of a DNA sequence under Evo2-7B (nucleotide-level log-likelihood via the byte-level tokenizer). Returns total_log_likelihood, mean_log_likelihood, scored_positions and optionally per_position_log_likelihood (position 0 is unscored by the causal shift; value k corresponds to 0-based position k+1). Use for sequence-level probability estimates. This is a DNA foundation model inference tool. It does not provide clinical diagnosis. Model scores should not be interpreted as pathogenicity labels without additional validation. |
| evo2_variant_scoreA | Compare a single-nucleotide variant: the Evo2-7B log-likelihood of the wildtype sequence vs the mutant sequence. Returns delta_log_likelihood (mutant − wildtype); a negative value means the mutant sequence is LESS likely under the model. Positions are 1-based by default (VCF-style). Variants at position 1 are rejected because a causal LM cannot score the first base. This is a DNA foundation model inference tool. It does not provide clinical diagnosis. Model scores should not be interpreted as pathogenicity labels without additional validation. |
| evo2_batch_scoreA | Score many single-nucleotide variants against one wildtype sequence. The WT forward pass is computed exactly once and reused; identical (position, alt) mutants are forwarded once; mutant requests run with bounded concurrency (EVO2_MCP_MAX_CONCURRENCY, default 2) to respect NVIDIA rate limits, and per-variant API errors are reported per-variant. Use for saturation-mutagenesis-style analyses. This is a DNA foundation model inference tool. It does not provide clinical diagnosis. Model scores should not be interpreted as pathogenicity labels without additional validation. |
| evo2_score_fastaA | Score every record in a FASTA source under Evo2-7B. Provide EITHER |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.