diversity_summary

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

No annotations provided, so description must carry full burden. It discloses that a CSV is written (non-destructive side effect) and mentions caching for VCF method. However, it does not clarify auth requirements, permission needs, or potential blocking behavior. Also does not explicitly state it is read-only (non-modifying).

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?

Description is extremely concise: two sentences for purpose and definitions, one sentence for file output, one sentence for optimization hint. No filler, front-loaded with key information.

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?

With output schema present, description need not explain return values. It covers statistics, output file, and optimization. However, it does not describe 'dataset means' or explain region/ output_dir parameters, which are left to schema. Slightly incomplete given 5 parameters.

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 100%, but description adds value by defining He, Ho, PIC (not in schema) and providing usage hints for method and max_markers (large set optimization). This goes beyond basic parameter descriptions.

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?

Description explicitly states tool computes per-marker diversity statistics (MAF, He, Ho, PIC) and dataset means. It lists specific statistics and mentions output file, clearly distinguishing it from sibling tools like diversity_by_group or diversity_fst.

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?

Provides explicit guidance for large datasets (use method='allelematrix' + max_markers), but does not compare directly to alternatives or state when not to use this tool. Context implies per-marker focus, but lacks explicit exclusions.

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