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

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

The description adds valuable behavioral context beyond annotations. Annotations indicate it's not read-only, destructive, or idempotent, and has open-world hints. The description clarifies that it submits a request and returns immediately with a prediction ID (asynchronous operation), specifies that model IDs must be exact, and outlines dependencies on other tools for proper usage. No contradiction with annotations exists.

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 (overview, important notes, args, returns, examples). Most sentences earn their place by providing essential information, though some redundancy exists (e.g., repeating atlas_get_model_info usage). It is front-loaded with key details about the asynchronous nature and model ID requirements.

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 complexity (asynchronous image generation with model-specific parameters), the description is highly complete. It covers purpose, usage workflow, parameter semantics, dependencies on sibling tools, and examples. Although there's no output schema, it clearly explains the return value (prediction ID) and how to use it with atlas_get_prediction, addressing all critical contextual gaps.

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 baseline is 3. The description enhances this by explaining that 'model' requires an exact ID and must be found via atlas_list_models, and that 'params' are model-specific JSON objects with common examples like 'prompt' and 'image_size'. It also directs users to atlas_get_model_info for full parameter lists, adding practical guidance beyond schema definitions.

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: 'Generate an image using Atlas Cloud API.' It specifies the exact action (generate), resource (image), and platform (Atlas Cloud API), distinguishing it from siblings like atlas_generate_video (video generation) and atlas_quick_generate (likely a simpler variant).

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 versus alternatives. It instructs to use atlas_list_models to find model IDs, atlas_get_model_info to understand parameters, and atlas_get_prediction to check results. It also warns against guessing model IDs, clearly defining prerequisites and workflow.

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