compare_experiments

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

The description discloses that it only shows deviating hyperparameters and not the full config, and presents metric results side by side. No annotations are provided, so the description carries the full burden, and it does this adequately, though it could mention if it's read-only or if data is mutated (it is likely read-only).

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 very concise, front-loads the purpose, then details behavior, and ends with parameter documentation. Every sentence is meaningful and well-structured.

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 tool's simplicity (2 parameters, no output schema), the description covers the main functionality, usage context, and parameter formats. It could be considered complete for this complexity level, though it might hint at the return format.

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?

Despite 0% schema description coverage, the description explains the parameter meanings: 'run_ids' is a list of run names, and 'projekt' is optional to narrow the search. This adds value beyond the schema's field definitions.

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 compares experiments and highlights differences, specifically showing deviating hyperparameters and metrics. It distinguishes itself from 'analyze_project', which is for an overall overview.

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 explicitly recommends using this tool to see the effect of single configuration changes and advises using 'analyze_project' for an overall overview, providing clear when-to-use and when-not-to-use guidance with an alternative.

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