Atlas Cloud MCP Server (Image / Video / LLM APIs)

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

The annotations already indicate this is a safe, read-only, idempotent operation with open-world behavior. The description adds valuable context beyond this by explaining the workflow: it describes statuses ('processing', 'starting', 'completed', 'succeeded'), recommends retry behavior, and provides actionable steps for handling completed results (show URLs, offer download). This enriches the agent's understanding without contradicting annotations.

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 well-structured and appropriately sized, with clear sections for purpose, usage instructions, and examples. It's front-loaded with the core purpose, and every sentence adds value, such as explaining status handling and result actions. It could be slightly more concise by integrating the example more seamlessly, but overall it's efficient.

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 the tool's moderate complexity (single parameter, no output schema), the description is complete. It covers the purpose, usage context, behavioral details like statuses and retries, and result handling steps. With annotations providing safety and idempotency hints, and the schema fully describing the parameter, no critical information is missing for an agent to use this tool effectively.

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?

The input schema has 100% description coverage, with the parameter 'prediction_id' clearly documented. The description adds minimal extra meaning by noting it's 'returned from a generation request' and providing an example, but this doesn't significantly enhance the schema's information. This meets the baseline of 3 for high schema coverage.

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 result') and resources ('image/video generation task'), distinguishing it from sibling tools like atlas_generate_image or atlas_generate_video which create tasks rather than monitor them. It explicitly mentions it's for use after submitting a generation request, which differentiates it from other tools.

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 explicit guidance on when to use this tool ('after submitting a generation request') and when not to use it (if status is 'processing' or 'starting', wait and try again). It also implicitly suggests alternatives by referencing generation requests, which likely come from sibling tools like atlas_generate_image or atlas_generate_video, though it doesn't name them directly.

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