Read normalized job state and polling guidance. No worker, provider, or progress-percent fields.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | FARPY job id. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose that this is a read operation and explicitly lists absent fields, which is useful. Still, it does not explain what 'normalized' means, how polling guidance is expressed, or what error/edge behavior might occur.
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 deliver the core action, the output scope, and a key limitation with no filler. The main verb and resource are front-loaded, and every sentence earns its place.
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 tool is simple: one required, well-documented parameter, an output schema is present, and the description covers purpose and limitations. It could more explicitly connect to farpy_start for job lifecycle context, but nothing needed to invoke it correctly is missing.
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%, and the single required parameter job_id is already documented as 'FARPY job id.' The description adds no additional parameter-level meaning, so the baseline 3 is appropriate.
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 states a specific verb and resource: 'Read normalized job state and polling guidance.' The negative clause 'No worker, provider, or progress-percent fields' further clarifies scope. It does not explicitly name a sibling, but the purpose is 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?
'Polling guidance' implies this tool should be used when checking job progression, and the excluded fields suggest when it is not appropriate. However, there is no explicit when-to-use or when-not-to-use guidance, nor any named alternative such as farpy_inspect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct phase of the rendering workflow: capabilities, upload, inspect, submit, status, download, receipt, and wallet. There is no overlap in purpose, making misselection unlikely.
All tools share the farpy_ prefix and snake_case, but some use verb_noun (inspect_blend, submit_render) while others are noun-only (capabilities, receipt, wallet). This is a minor inconsistency but the pattern is still readable.
Eight tools is well within the optimal range for a focused cloud rendering service, covering the full lifecycle without unnecessary bloat. Each tool earns its place.
The toolkit covers the entire render pipeline from upload to payment receipt, with no obvious dead ends. A cancel operation is the only notable gap, but it is not integral to the core workflow.
