get_sql_generation_prompt

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

No annotations are provided, so the description carries full burden. It states the tool returns a prompt and schema info, but does not disclose behavioral details like rate limits, authentication requirements, or error handling. It provides minimal transparency beyond the basic function.

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 well-structured with clear sections in Japanese and English, and front-loads the core purpose and usage. It is slightly verbose with examples but the examples are useful. Every sentence 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 only one parameter, no output schema, and no annotations, the description covers the essential aspects: what it does, what it returns, and how to use the result. It lacks detail about the exact return format but is sufficient for a tool that generates prompts for LLM consumption.

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 0%, but the description compensates by explaining the 'query_text' parameter with natural language examples. The description adds meaning beyond the bare schema type definition, helping the agent understand the format and scope of input.

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 tool generates LLM prompts for converting natural language to SQL, and explicitly distinguishes itself from direct SQL execution tools like keiba_data_search, which is listed as a sibling. The verb 'generate' and resource 'LLM prompt for SQL' are specific and unambiguous.

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 provides explicit guidance: 'このツールはSQLを直接実行しません' (this tool does not execute SQL) and '生成されたSQLは keiba_data_search ツールで実行してください' (execute the generated SQL using keiba_data_search). This tells the agent when to use this tool and what alternative to use for execution.

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