check_job

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it's a read operation (checking status), returns specific status values (queued, processing, completed, failed), progress percentage, and result data including download URLs. However, it doesn't mention error handling, rate limits, authentication needs, or whether the job ID must be from the current session.

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 perfectly concise and front-loaded: the first clause states the core purpose, followed by essential details about return values. Every sentence earns its place with no wasted words, and the structure moves from general to specific efficiently.

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 single-parameter read tool with no output schema, the description provides good completeness: it explains the purpose, when to use it, and what it returns. However, it doesn't specify the format of result data or download URLs, and with no annotations, some behavioral aspects like error cases remain uncovered.

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 100%, so the schema already documents the single parameter 'job_id' as 'The job ID returned by a previous tool call.' The description adds context by listing which previous tools (run_algorithm, protect_media, detect_ai) provide these job IDs, which adds some semantic value beyond the schema. Baseline 3 is appropriate when schema coverage is high.

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 purpose with specific verbs ('check the status') and resources ('asynchronous job'), and distinguishes it from siblings by listing the specific job-creating tools (run_algorithm, protect_media, detect_ai). It goes beyond a simple tautology of the name 'check_job' by specifying what kind of job it checks.

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 context on when to use this tool: after calling run_algorithm, protect_media, or detect_ai, when you have a job ID. It implicitly distinguishes from siblings by referencing those specific tools. However, it doesn't explicitly state when NOT to use it or mention alternatives for job status checking.

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