llm_job_submit

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

With no annotations, the description carries the full burden, but it only says 'submit a job,' leaving ambiguous whether it is asynchronous, what it returns, whether it triggers costs, or what side effects occur. The 'confirm' parameter in the schema suggests potential cost confirmation but is not addressed.

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 one concise sentence with no filler or unnecessary detail. It is appropriately front-loaded but somewhat too terse to convey all necessary context.

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 six parameters, no annotations, and minimal description, the tool's full behavior and input semantics are far from adequately captured. The presence of an output schema mitigates return-value ambiguity but does not compensate for missing parameter context.

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% and the description offers no explanation for any of the six parameters (url, query, schema, confirm, provider, webhook_config). The required fields 'url' and 'query' are self-explanatory from context but not explicitly defined.

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 resource (Crawl4AI LLM extraction job), and the context (configured native server). The phrase 'LLM extraction job' distinguishes it from sibling crawl_job_submit, making the tool's unique role apparent.

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 no explicit guidance on when to choose this tool over alternatives such as crawl_job_submit or extract. No usage context, prerequisites, or exclusions are mentioned.

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