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

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

Annotations already indicate this is a safe, read-only, idempotent, and open-world operation. The description adds context by specifying that it fetches OpenAPI schema and generates documentation with cURL examples, which provides useful behavioral details beyond the annotations. However, it doesn't mention potential rate limits, authentication needs, or error conditions.

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 with clear sections (purpose, args, returns, examples) and uses bullet points for readability. It's appropriately sized for the tool's complexity, though the 'Returns' section is slightly verbose. Most sentences add value, but there's minor repetition in the examples.

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, read-only operation) and rich annotations, the description is mostly complete. It details the output format (markdown with specific content) and provides examples. However, without an output schema, it could benefit from more explicit return structure details or error handling information.

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, fully documenting the single 'model' parameter. The description adds minimal value beyond the schema by listing example model IDs and noting the parameter is required, but doesn't provide additional syntax, format details, or constraints. With high schema coverage, the baseline score of 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 clearly states the tool's purpose with specific verbs ('Get detailed information', 'fetches', 'generates') and identifies the resource ('a specific Atlas Cloud model'). It distinguishes this from sibling tools like atlas_list_models (which lists models) and atlas_get_prediction (which runs predictions), making the scope and differentiation explicit.

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 implies usage context by specifying what information is returned (metadata, pricing, schema, examples), suggesting it's for understanding model capabilities before use. However, it doesn't explicitly state when to use this tool versus alternatives like atlas_list_models (for browsing) or atlas_search_docs (for documentation), nor does it mention prerequisites or exclusions.

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