get_analogy_reasoning_scaffold

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

No annotations provided, so description carries full burden. It clearly states the tool is read-only and describes what it returns (checklists, verification steps, comparison guidance, uncertainty policy). It also explicitly states what it does not do, preventing misuse. No contradictions.

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 sentences deliver purpose, content, and non-usage in a front-loaded manner. Every word adds value; no redundancy.

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 zero parameters and presence of an output schema, the description explains the key contents of the scaffold (checklists, steps, etc.). It is sufficient for an agent to understand what the tool provides. Could be slightly more detailed about the format, but overall complete.

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?

No parameters (0 param count), so baseline is 4. Description adds meaning by explaining the return value components, which is valuable since there are no parameters to document.

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 returns a read-only method scaffold for analogy reasoning, specifies the components (relation-type checklists, verification steps, etc.), and explicitly distinguishes from solving tools by stating it does not solve questions or select an answer. This differentiates it from sibling tools like solve_logic_reasoning and solve_data_analysis.

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 usage context: use for obtaining a reasoning scaffold, not for solving. It explicitly says 'does not solve questions or select an answer,' which guides agents away from using it for final answers. However, it does not provide explicit when-to-use vs. alternatives or mention when not to use it.

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