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

With no annotations, the description carries the full burden. It discloses that the operation is asynchronous (returns a taskUid for later polling) and notes specific format constraints. However, it does not address side effects, required permissions, or failure modes, so transparency is partial.

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 concise and well-structured, starting with the core purpose and then providing necessary details. Every sentence adds value, with no filler or repetition.

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?

Despite the absence of an output schema, the description covers the essential workflow: return value, polling steps, and result retrieval. It explains key parameters and caveats. Some minor gaps remain (e.g., other parameters), but the overall context is sufficient for correct usage.

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 schema has 58% parameter coverage, and the description significantly compensates by explaining the parsers stack structure with a concrete example, the meaning of $p1 in results_format, and the macro behavior of results_file_name. This adds substantial meaning beyond 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 queues a bulk parsing task and returns the taskUid. It explicitly distinguishes the workflow from sibling tools by mentioning polling with task_state/wait_task and fetching output with task_results, making the 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 provides clear usage context: queue a task, then poll with task_state/wait_task, then fetch with task_results. It does not explicitly mention alternatives like one_request, but the 'bulk parsing task' phrasing conveys when this tool is appropriate.

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