list_experiments

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

No annotations are provided, so the description bears full burden. It discloses that reading is instant (no JVM start), that it reads without side effects, and describes the precise return format. It could be improved by explicitly stating that the tool is read-only, but the description sufficiently conveys the behavioral traits.

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 and front-loaded with the core purpose. Every sentence adds value: purpose, mechanism, default behavior, parameter usage, and output format. No superfluous text.

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 (1 optional parameter, no annotations, but detailed output), the description covers input, default behavior, output format, and edge case (empty list). It also mentions relationship to `run_experiment`, making it contextually complete.

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 has 0% description coverage, but the description compensates by explaining that `model_path` is optional and defaults to the most recently loaded model. It gives context on when to use the parameter (to inspect a specific file), adding significant meaning beyond the schema 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 the tool lists BehaviorSpace experiments saved inside a NetLogo model file, specifying it reads the `<experiments>` section. It distinguishes from siblings like `run_experiment` by noting it returns the list of saved experiments and mentions that an empty list means no saved experiments, but inline specs can still be passed to `run_experiment`.

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 explains default behavior (inspects most recently loaded model) and how to use the `model_path` parameter to inspect a specific file. It implies usage before `run_experiment` but does not explicitly compare to `preview_experiment` or other siblings, nor does it state when not to use this tool.

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