refine_thought

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

No annotations are provided, so the description carries the full responsibility for disclosing behavior. It reveals the operation uses an LLM and aims to improve alignment, but it does not explain whether the original thought is modified in place, whether a new thought is created, whether the original is preserved, or what the return value is. This missing side-effect and outcome information is a significant gap for a tool that presumably mutates the thought tree.

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 short sentence with the verb and resource placed at the beginning, and every word contributes to the meaning. 'Using LLM' is slightly redundant within an LLM-based system but is not wasteful. The structure is easily scannable and appropriately concise.

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?

For a two-parameter tool with no annotations and no output schema, the description is too sparse. It does not specify return values, side effects, preconditions (e.g., whether the thought must already exist in the tree), or how the result is surfaced. While the schema is complete for parameter names, the behavioral context is insufficient for an agent to call the tool with full confidence.

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% and both parameters (treeId, thoughtId) are clearly documented as IDs. The description adds no parameter-specific detail beyond the general notion of 'a thought' and 'the goal', so it does not enhance the schema. Baseline 3 applies because the schema already handles parameter semantics.

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 uses a specific verb ('refine') and resource ('a thought') and states the objective ('better align with the goal'), which clearly conveys the intended action and differentiates it from evalution, verification, and selection operations. However, it doesn't explicitly reference sibling tools, and 'the goal' is an assumed context that could be ambiguous without surrounding system knowledge.

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 phrase 'to better align with the goal' implies a use case: when a thought needs adjustment toward the goal. However, there is no explicit when-to-use versus when-not-to-use guidance, no preconditions, and no comparisons to alternatives such as evaluate_thought or self_reflect_thought. The context is implied rather than clearly spelled out.

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