evo2_score

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

With no annotations, the description carries full behavioral burden. It discloses output structure, the causal shift causing position 0 to be unscored, optional per-position output, and explicitly warns against clinical diagnosis and pathogenicity interpretation. This is thorough and transparent for a model-scoring tool.

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

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

The description is a few sentences but each adds necessary context: purpose, output details, usage, and disclaimers. It is not overly verbose and front-loads the core function. Slightly dense with parentheticals, but still efficient.

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

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

Given the absence of an output schema, the description fully explains the returned fields (total_log_likelihood, mean_log_likelihood, scored_positions, optional per_position_log_likelihood) and the positional convention. It also addresses interpretation caveats. For a single-sequence inference tool, nothing critical is missing.

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

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

Schema description coverage is 100%, so both parameters are well-documented in the schema. The description adds little beyond the schema; it does explain the per-position output semantics within the overall description, but that is not parameter-specific. Baseline 3 is appropriate.

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

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

The description clearly states the tool computes model-based log-likelihood of a DNA sequence under Evo2-7B, with specific output fields. It does not explicitly differentiate from siblings like evo2_batch_score or evo2_variant_score, but the single-sequence focus is implied by the parameter list and usage text.

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

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

It provides a clear context: 'Use for sequence-level probability estimates' and notes this is a DNA foundation model inference tool. It does not explicitly state when not to use it or mention alternatives, but the use case is specified clearly enough without being misleading.

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