evo2_batch_score

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

With no annotations, the description carries the full burden of disclosure, and it excels. It reveals the caching behavior (WT pass computed once, identical mutants forwarded once), concurrency limits (EVO2_MCP_MAX_CONCURRENCY, default 2) to respect NVIDIA rate limits, and per-variant error reporting. It also adds crucial disclaimers (not clinical diagnosis, not pathogenicity labels). This is exemplary transparency.

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 dense but every sentence earns its place. It leads with the core purpose, then efficiently packs performance details, error handling, and disclaimers. It is slightly longer than minimal but avoids fluff. The structure is logical: purpose → efficiency → safety/limitations. This is good, though a bit dense; a 4 feels right.

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?

For a batch scoring tool with no output schema, the description covers the execution semantics, concurrency, error reporting, and clinical disclaimer. However, it omits any description of the return format or how results are structured (even per-variant errors are mentioned but not the shape). Given the complexity and no annotations, the absence of output details leaves a small but notable gap, so it stops short of a 5.

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 67% (sequence and variants have descriptions; coordinate has an enum with a default). The description does not add parameter-level meaning beyond what the schema already provides—it merely restates the variants structure and coordinate default implicitly. It does clarify the 'many' scope, but that is more behavioral than parameter-specific. Baseline 3 is appropriate since the schema covers most parameters.

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 opens with a strong, specific verb-resource pair: 'Score many single-nucleotide variants against one wildtype sequence.' It clearly states the batch scope and distinguishes itself from single-variant tools by emphasizing 'many' and 'batch.' It also specifies the analysis type (saturation-mutagenesis), which orients the agent on the intended use case.

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 tool provides a clear when-to-use directive: 'Use for saturation-mutagenesis-style analyses.' It also implies batching by explaining the reuse of the WT forward pass and bounded concurrency. However, it does not explicitly name alternative sibling tools for single-variant scoring, so the agent must infer those from the 'many' qualifier. This is solid but not fully explicit.

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