get_experiment

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

Without annotations, the description carries the burden. It describes the tool as returning data (implies read-only), but does not mention potential errors, authentication, or side effects. However, the behavior is straightforward for a retrieval tool.

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?

Three concise sentences: purpose, usage guidance, parameter details. No unnecessary words. Information is front-loaded 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 no output schema, the description mentions the return of hyperparameters and results but does not detail the structure. For a simple get tool, this is mostly complete, though additional output format could be helpful.

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 0%, so the description adds value by explaining run_id as 'name of the run' with an example, and projekt as optional to narrow search. This provides meaning beyond the schema's type alone.

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 returns hyperparameters and results of a single run. It distinguishes from sibling tools like list_experiments (which lists runs) and analyze_project (project-level analysis).

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?

Explicitly advises to first use list_experiments or analyze_project to obtain valid run_ids. This provides clear when-to-use guidance and the prerequisite for tool invocation.

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