spraay_compute_status

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value beyond these by noting the cost ($0.001 USDC) and the polling nature, which implies idempotency and no side effects. No contradictions.

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 three concise sentences, each adding distinct value: purpose, use case, and cost. No redundant or vague language; all sentences earn their 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?

For a simple polling tool with one parameter and an output schema, the description covers purpose, use cases, and cost. It does not explain polling behavior (e.g., retry logic) or error handling, but the output schema likely provides return format details. Adequately complete for a low-complexity tool.

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?

With 100% schema description coverage, the parameter 'jobId' is already well-documented in the schema as 'Job ID or prediction ID returned from a compute request'. The description adds only marginal context by linking the parameter to the use cases (video generation, batch jobs), which is helpful but not essential.

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 polls async compute job status and specifies it is for video generation and batch jobs that return 'processing'. This distinguishes it from sibling tools like spraay_compute_video_generation (which likely triggers jobs) and spraay_compute_batch (batch execution). The verb 'poll' and resource 'status' are specific.

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 explicitly says 'Use for video generation and batch jobs that return processing', providing clear context for when to use this tool. It does not mention when not to use it or list alternatives, but the provided use cases are sufficient guidance for the intended scenario.

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