get_fold_summary

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

Without annotations, the description discloses that it returns only summary statistics (not raw values) and automatically warns about high variance across folds, providing useful behavioral context beyond the schema.

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 concise, well-structured with a clear purpose, output details, and argument list. Every sentence adds value without redundancy.

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 no output schema and no annotations, the description covers the tool's purpose, return values (summary stats), and parameters adequately. It lacks explicit return format, but is sufficient for typical use.

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 0%, but the description adds meaning by explaining run_id with an example ('dcgan_run_005') and clarifying projekt as optional for narrowing search, compensating for the lack of schema documentation.

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 it aggregates k-fold cross-validation results into summary statistics (mean, std, min, max per metric), distinguishing it from sibling tools like get_experiment or compare_experiments.

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 usage for obtaining aggregated fold results and warns about high variance, but does not explicitly state when to use vs alternatives or provide exclusion criteria.

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