generate_children_with_llm

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

With no annotations, the description carries the full burden. It discloses that actual API calls are made, which is useful, but lacks detail on side effects (e.g., are child thoughts persisted?), error handling, or any destructive behavior. Adequate but not thorough.

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?

Two concise sentences that front-load the core purpose and key behavioral trait (API calls). Every sentence adds value; no wasted words.

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 four parameters and no output schema, the description explains the action but lacks context on what 'child thoughts' are, how they integrate into the tree, or what the response contains. Adequate minimal coverage but incomplete for full understanding.

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 100%, so each parameter is already described in the schema. The description adds no additional meaning beyond what the schema provides, such as constraints or formatting for 'numChildren' or 'temperature'.

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 verb ('generate') and resource ('child thoughts') and specifies the mechanism ('using the configured LLM provider'). It distinguishes from siblings like 'promote_thought_to_tasks' by focusing on LLM-based generation of child thoughts.

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?

No guidance on when to use this tool versus alternatives (e.g., 'spawn_tot_from_task', 'promote_thought_to_tasks'). No mention of prerequisites or conditions under which generation should occur.

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