Server Quality Checklist

Tool Scores

• queryA
  4.8/5.0
  Behavior5/5

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

  With no annotations provided, the description carries the full burden and does so thoroughly. It discloses that the agent explores the schema, writes and executes SQL, self-corrects on errors, caps results at max_rows, and returns a detailed dict including sql, data, error, row_count, and tool_calls_made. This is exemplary behavioral transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is well-organized with a lead sentence for the main purpose, a brief behavioral paragraph, and clearly labeled Args/Returns sections. Every sentence adds useful information; nothing is redundant or filler. The format is long enough to be complete but remains scannable.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  There is no output schema, so the description's detailed Returns section is essential and well executed. It explains the return dict fields, row count, error behavior, and internal tool calls. Combined with the parameter explanations and read-only guarantee, the description fully covers what an agent needs to invoke and interpret the tool correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Although the schema only lists types and a default, the description explains the meaning of each parameter: 'question' is a natural-language query with an example, and 'max_rows' caps the returned data with a default of 100. It also clarifies how max_rows affects the output, which is additional semantic value beyond the raw schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's function: 'Ask the database a natural-language question.' It goes further to describe the internal process (explore schema, write SQL, execute, self-correct) and explicitly scopes it as read-only with SELECT-style statements. This makes the purpose unmistakable even without sibling tools.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description establishes clear context by presenting the tool as a natural-language interface to the database and by noting that it only executes SELECT statements. It implicitly says not to use it for writes or mutations, though it does not explicitly name alternative tools or spell out when-not-to-use scenarios in more detail.

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