dataprobe_ask

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait (polling until the answer is finished), which adds value beyond the schema. However, it omits details about return values, error handling, side effects, or safety, leaving significant behavioral aspects undisclosed.

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 a single sentence, front-loaded with the action ('Ask a natural-language data question') and the polling behavior. No redundant words or information; it is appropriately concise.

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 no output schema and a polling-centric tool, the description fails to explain what the tool returns after polling finishes (e.g., the answer itself or a status). It also does not address potential timeouts or failure modes. This is adequate for basic invocation but has clear gaps.

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 coverage is 100%, with all parameters described in the input schema (e.g., query, history, max_polls, dataset_id, poll_interval_ms). The description adds no additional parameter-level detail, so the baseline score of 3 applies.

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?

Description uses a specific verb 'Ask' and resource 'natural-language data question', and clearly indicates a polling behavior ('poll until the DataProbe answer is finished'). This distinguishes it from sibling tools like dataprobe_get_ask_result, making its purpose 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 clearly conveys when to use this tool: to ask a natural-language question and wait for completion via polling. It does not explicitly mention alternatives or when-not-to-use, but the context is strong enough given sibling names and the stated polling behavior.

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