list_experiments

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

With no annotations, the description carries full burden. It states it's an 'Ueberblick' (overview) and lists fields (model, status, date), but does not disclose pagination, ordering, or any limits. Adequate but not fully transparent.

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?

Four sentences, no wasted words. Purpose, usage guidance, and parameter explanation are concise and well-structured. Slightly verbose with German phrasing but still efficient.

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 (one optional param, list output) and existence of an output schema (not shown), the description sufficiently covers key aspects: what it lists, filtering, and sibling distinction. Could mention sorting or default ordering but complete enough.

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%, so description must compensate. It explains 'projekt' parameter: optional, restricts to a project, provides example ('masterarbeit'), and clarifies that omitting shows all runs. Adds meaningful context beyond bare schema.

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 lists training/test runs with model, status, and date. It distinguishes from siblings (analyze_project for total analysis, get_experiment for individual runs), making the tool's specific purpose unambiguous.

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 tells when to use this tool ('Nur der Ueberblick') and when to use alternatives ('Fuer eine Gesamtanalyse... analyze_project... fuer einen einzelnen Lauf get_experiment'). Also hints at optional filtering with 'projekt' parameter.

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