suggest_discriminating_experiment

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

No annotations are provided, so the description must carry the full burden. It discloses the internal decision logic (re-test conflict suspect at depth, otherwise closest alternative), which is good, but it does not state whether the tool is read-only, has side effects, or what the output format is. Since 'suggest' implies non-mutating, the description is adequate but not rich.

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 a single, front-loaded sentence with no wasted words. It immediately states the purpose and then specifies the decision rule, earning its place efficiently.

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?

The tool has no output schema and no annotations, yet the description does not explain what the agent will receive (e.g., a string, a structured object) or any prerequisites. However, it gives a clear decision rule for a zero-input tool, so it is reasonably complete for its simplicity but leaves return-value expectations open.

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?

The tool has zero parameters, so schema coverage is vacuously 100%. The baseline for 0 params is 4, and the description adds no parameter-specific detail because none exist. The description's context about conflict resolution complements the empty schema without redundancy.

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 a specific verb ('Propose') and resource ('the single most informative next experiment'), with a clear decision rule distinguishing it from generic tools. However, it does not explicitly contrast with sibling tools like get_next_targets, so differentiation is only implicit.

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 when to use (when you need to select the next experiment) and even provides algorithmic guidance, but it does not state explicit alternatives or when not to use the tool. Sibling comparisons would strengthen this.

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