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evo2_forward

Run a forward pass of Evo2-7B on a DNA sequence and return per-layer summary statistics or raw tensors for analysis, including logits, attention, MLP, or embedding outputs.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosummary: per-layer shape/dtype/min/max/mean/std only. raw: inline the tensors as nested lists (small only, capped). save: always write an .npz file in the output dir and return its path.summary
sequenceYesDNA sequence over {A,C,G,T}. Lowercase and whitespace are normalised. IUPAC ambiguity codes (N, R, Y, ...) are rejected unless the server was started with EVO2_MCP_ALLOW_AMBIGUOUS=1 (N only).
save_pathNoOptional filename for the saved NPZ (implies mode='save'). Must resolve inside the server's output directory (EVO2_MCP_OUTPUT_DIR, default ./output). If omitted, a timestamped file is used.
output_layersNoEvo2 layer names. The final logits layer is 'output_layer' on self-hosted NIM 2.x containers, but the hosted health.api.nvidia.com endpoint uses model attribute names — use 'unembed' for final logits (also 'embedding_layer', 'norm', 'blocks.N.mlp'). Scoring tools auto-detect this. See https://docs.nvidia.com/nim/bionemo/evo2/latest/endpoints.html
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries full responsibility. It discloses mode behavior (summary is context-safe, raw only for small tensors, save writes to server output dir), ambiguity-code handling with an environment flag, endpoint-dependent layer naming, and the clinical limitation. This is exceptionally transparent for a complex inference tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, then organizes modes and constraints in a logical flow. Every sentence provides distinct information—no filler. Dense but efficient, appropriate for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with four parameters, no output schema, and no annotations, this description is remarkably complete. It covers return types, mode constraints, output directory rules, ambiguity handling, endpoint differences, and disclaimers—everything an agent needs to invoke it correctly without external documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions already cover all parameters (100% coverage), but the description adds significant value: it explains the endpoint-specific layer names (output_layer vs unembed), the save_mode implication of save_path, and the ambiguity environment flag. This goes beyond the schema to make parameter usage clearer.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Run a forward pass of Evo2-7B') and explicitly contrasts with scoring tools: 'Use this when you need layer outputs for analysis, not just a scalar score.' This clearly distinguishes from sibling scoring tools and conveys the tool's core purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use this tool versus alternatives ('not just a scalar score'), and details the three modes with their intended contexts. It also adds a clinical disclaimer ('does not provide clinical diagnosis'), guiding appropriate usage boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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