reason_together

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

No annotations are provided, so the description carries full burden. It thoroughly discloses the workflow steps for each strategy, the number of rounds, the roles of models and critic, and that the output is a synthesis. This equips the agent with key behavioral insights.

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 essential purpose, followed by a clean list of strategies. No redundant sentences; every part adds value.

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 complexity (8 parameters, multi-step workflow) and the presence of an output schema, the description covers the workflow, strategies, and return values (trace, individual responses, synthesized answer), providing sufficient context for correct invocation.

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 100%, so baseline is 3. The description adds context about strategy behavior but does not significantly enhance understanding beyond the schema's parameter descriptions.

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 is a 'multi-step reasoning workflow across multiple models' and lists three specific strategies (independent_then_critique, debate, red_team), which distinguishes it from simpler sibling tools like ask_model or ask_many.

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 each strategy and their behavior (e.g., 'models answer independently, then a critic synthesizes') and notes that the final answer is 'synthesis, not ground truth'. However, it does not explicitly state when to prefer this tool over alternatives like ask_many or pick_best_answer.

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