query

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description's behavioral burden is lower. The description adds value by disclosing the exact size of the table ('61.8 million rows'), the cost profile ('local, so a query costs no network'), and the fact that results are capped (25,000 characters, max rows 500). This goes beyond basic safety disclosure to provide performance 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 well-structured with a clear introductory sentence, a list of table contents, a callout to call schema() first, and a recommendation to use the results view. Every sentence adds value. It could be slightly more concise by removing the exact row count and focusing on the most critical guidance, but it's not overly verbose.

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 the complexity of the domain (61.8 million rows, multiple dimensions per cell), the rich annotations (readOnly, openWorld, idempotent), a fully described 2-parameter schema, and the presence of an output schema, the description is remarkably complete. It explains the exact contents, potential pitfalls (semantic traps), and best practices. There is no need to discuss return values since an output schema exists.

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?

The input schema already provides clear descriptions for both parameters (sql and max_rows) with examples and constraints. Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond what the schema provides, as the schema already explains the allowed SQL statements, default row count, and character limit. The description's mention of capping (500 rows, 25,000 chars) is redundant with the schema.

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 runs 'read-only SQL over the whole BRaVa gene-level results table.' It specifies the verb ('query'), resource ('BRaVa gene-level results table'), and scope ('read-only SQL'). While the name 'query' is generic, the description distinguishes it from siblings by specifying the exact domain (BRaVa) and table type (gene-level results), making it clear what this tool does compared to 'variant' or 'schema'.

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 explicitly advises to 'call `schema()` first' to understand the correct tables, columns, and semantic traps. It warns about semantic traps that can 'invert the answer.' It recommends querying the `results` view instead of raw tables. This provides explicit when-to-use guidance and a prerequisite action (calling schema()), which is excellent for avoiding misuse.

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