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

The description conveys that this creates a new job (implying asynchronous processing) rather than returning a PDF immediately, and the optional webhook is visible in the schema. However, with no annotations at all, it does not disclose side effects like job persistence, processing time, or how results are retrieved.

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?

Two concise sentences convey the action, the input format, and the per-line behavior without repetition or fluff. The structure is efficient and easy to parse.

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?

The description covers the core invocation well and the schema is fully described, but it does not mention whether the job processes asynchronously, how results are retrieved (e.g., job ID), or when to prefer this over sibling tools such as generate_preview. It is sufficient for a basic call but leaves workflow context implicit.

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 description adds meaningful context beyond the schema by explaining that each JSON line includes variable values applied to the template essere. While the schema already covers the JSONL format well, the 'variable values' phrase clarifies the semantic purpose of the data, which is useful for constructing valid input.

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 action ('submit'), the object ('a new PDF generation job'), and the required inputs ('template' and 'JSONL string'). It does not explicitly contrast itself with sibling tools like generate_preview, but the phrase 'new job' conveys a batch/async operation distinct from simple retrieval or preview tools.

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 gives no guidance on when to use submit_job versus alternatives such as generate_preview or how to retrieve job results. It implies batch generation from JSONL lines, but does not explicitly state that this is for bulk/multiple PDFs or mention a lighter-weight preview option. The agent must infer usage from the description and sibling tool names.

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