evo2_variant_score

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

With no annotations, the description fully discloses behavioral traits: it explains the output meaning (negative delta indicates lower likelihood), the reasoning behind rejecting position 1 (causal LM limitation), coordinate system default, and important limitations (not clinical, not pathogenicity). This goes well beyond a basic verb+object statement and gives the agent a clear model of the tool's behavior.

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 four sentences with no redundancy. It front-loads the core purpose and output interpretation, then adds constraints and domain caveats. Every sentence carries meaningful information, and the structure makes it easy for an agent to quickly grasp the tool's role.

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?

Despite having no output schema or annotations, the description covers the essential aspects: what it computes, how to interpret the result, coordinate system, positional restriction, and domain limitations. It does not explicitly describe the effect of include_per_position (though schema notes default false), but that is a minor gap. Overall, an agent has sufficient context to call the tool correctly.

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 coverage is high (83%), so the baseline is 3. The description adds semantic value by explaining the output delta and the coordinate system, but it does not elaborate on parameters that the schema already documents. It reinforces the 'sequence' as wildtype context, which is consistent with schema descriptions, but adds no new parameter-level nuance beyond the position-1 rejection rule.

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 states a specific action ('Compare a single-nucleotide variant') and defines the exact output ('delta_log_likelihood (mutant − wildtype)'). It clearly distinguishes the tool from siblings by focusing on variant comparison rather than generic scoring or batch operations. The inclusion of the model name (Evo2-7B) and the caveat about clinical diagnosis further clarify its purpose.

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?

The description implies when to use the tool (for single-nucleotide variant scoring) but does not explicitly compare with siblings like evo2_score or evo2_batch_score. It does provide usage constraints (1-based positions, rejection of position 1, not for clinical diagnosis) that help an agent decide applicability, yet it lacks an explicit 'use when' versus 'use instead' statement.

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