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: it explains the model search mechanism ('searches for models by keyword matching against model ID, display name, and tags'), clarifies the workflow ('After getting the prediction ID, use atlas_get_prediction to check the result'), and provides failure handling guidance. While annotations cover basic hints (non-readOnly, non-destructive, non-idempotent, openWorld), the description enhances understanding of the tool's operational behavior.

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 front-loaded: the first sentence clearly states the core functionality, followed by important usage notes, then parameter explanations with examples. Every sentence serves a purpose - there's no redundant information, and the formatting with bullet points enhances readability without wasting space.

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 (model discovery, parameter building, task submission) and the absence of an output schema, the description provides excellent contextual completeness. It explains the full workflow from input to result checking, includes failure handling, provides parameter guidance, and references relevant sibling tools. The examples further clarify usage in various scenarios.

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, but the description adds meaningful context: it provides concrete keyword examples ('Nano Banana', 'Seedream', 'Kling', 'Vidu', 'Seedance'), clarifies that extra_params can override defaults, and includes multiple usage examples that illustrate parameter combinations. This goes beyond the schema's basic descriptions.

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: 'One-step image or video generation - automatically finds the model by keyword, fetches its schema, builds parameters, and submits the task.' This specifies the verb ('generates'), resource ('image or video'), and distinguishes it from siblings like atlas_generate_image/video by emphasizing the automated model discovery and parameter building process.

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: 'If this tool fails to find a model, call atlas_list_models first to get the exact model list, then use atlas_generate_image or atlas_generate_video with the exact model ID instead.' It also mentions using atlas_get_prediction to check results, creating a clear workflow with sibling tools.

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