batch_submit_job

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

With no annotations, the description carries full burden. It discloses that the job is asynchronous and returns a job ID and status, but lacks details on job lifecycle, error handling, rate limits, or prerequisites. This is adequate but not comprehensive.

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 very concise—three short sentences that front-load the key information: submission action, return values, and async nature. No wasted words.

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?

For a complex tool with 13 parameters and no output schema, the description is too sparse. It doesn't explain the batch job concept, how to use the returned job ID (e.g., with batch_list_jobs), or example values. The lack of an output schema increases the need for descriptive context, which is not provided.

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%, so the description doesn't need to add parameter details. It provides no additional context beyond the schema, meeting the baseline expectation.

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's action: submitting a batch data download job for large historical datasets. It specifies the return values (job ID and status) and that processing is asynchronous, distinguishing it from sibling tools like batch_download (likely synchronous) and batch_list_jobs (listing jobs).

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 implies this tool is for large datasets and asynchronous processing, but it doesn't explicitly state when to use it over alternatives like batch_download or other data retrieval tools. No exclusions or specific context are provided, leaving the agent to infer usage.

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