ask

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

With no annotations, the description carries the full behavioral burden and does well: it discloses that a query is executed and the SQL is returned, that results are capped at 50 rows (implying truncation), and that `needs_clarification` is a distinct result state rather than a failure. It does not state whether execution is read-only or what permissions are required, which is the remaining gap for an unannotated query tool.

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?

Front-loaded with the core action, then return shape, then parameter semantics, then the clarification sentinel, in the order an agent needs them. No sentence is padding, and the parenthetical 'when any' efficiently conveys that not all queries produce SQL.

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?

An output schema exists, so the return-value explanation is partly redundant, but the description adds the meaningful behavioral detail that results are capped at 50 rows and that `needs_clarification` is actionable. For a three-parameter read-oriented tool, the only material omission is permission or read-only behavior.

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%, so the description must compensate, and it explains all three inputs: `workspace_id` scopes the question to a tenant workspace, `locale` takes 'en-GB' or 'en-US' and controls the answer's wording, and `question` is the plain-English query. The only shortfall is that it does not clarify what happens when `workspace_id` is omitted in a workspace-enabled tenant.

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 opens with a specific verb and resource: 'Ask a plain-English question about the connected data', which tells an agent exactly what class of operation this is (natural-language query over tenant data). It also references `whoami` for workspace discovery, so an agent can distinguish this from the sibling without opening either 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?

It gives concrete usage flow: when `needs_clarification` comes back, ask the user and re-call with the added detail, and it points to `whoami` when a tenant uses workspaces. What is missing is any negative guidance, such as what kinds of questions this should not be used for, so the agent must infer the boundary itself.

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