refine_prompt

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

No annotations are provided, so the description must disclose behavioral traits. It mentions 'using semantic memory' but does not explain side effects (e.g., memory updates), performance implications, or whether the operation is read-only or modifying. This leaves the agent uncertain about expectations.

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, efficient sentence that starts with the verb. No unnecessary words or structure.

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 no output schema and simple input, the description should explain what 'refinement' means and how semantic memory is used. It does not specify the output format or any side effects, leaving the agent without enough context to invoke the tool correctly.

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% for the single parameter, so baseline is 3. The description does not add any meaning beyond the schema's description of 'The original prompt that needs refinement.' No additional constraints, formats, or examples are given.

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 'refines' and the resource 'prompt', and uses 'semantic memory' to add specificity. It distinguishes from sibling 'learn_context' by focusing on prompt refinement rather than context learning.

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 'learn_context' or other alternatives. The description implies it is for making prompts more contextual and efficient, but does not specify prerequisites, exclusions, or when refinement is appropriate.

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