Sauce Labs MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a read operation (implied by 'shows'), works only for VDC jobs, may fail with 'asset not found' for RDC jobs, and returns structured JSON data. However, it doesn't mention rate limits, authentication needs, or error handling beyond the specific asset-not-found case.

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 with the core purpose, followed by important usage notes and parameter details. Every sentence adds value: the first states the purpose, the second and third provide critical usage guidelines, and the last clarifies parameters and return values. No wasted words.

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 (single parameter, VDC/RDC distinction) and the presence of an output schema, the description is complete. It covers purpose, usage guidelines, behavioral constraints, parameter semantics, and return value overview. The output schema handles return structure details, so the description doesn't need to duplicate that 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 schema description coverage is 0%, so the description must compensate. It provides a parameter description for 'job_id' with semantics ('The Sauce Labs Job ID (VDC jobs only)'), adding crucial context about the parameter's purpose and constraints. However, it doesn't detail the parameter's format or validation rules, leaving some gaps.

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 ('shows the complete log') and resources ('Sauce Labs test'), distinguishing it from siblings like get_specific_real_device_job_asset. It specifies the output format ('structured json format') and scope ('VDC jobs only'), making the purpose unambiguous and differentiated.

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 ('VDC jobs only') and when not to ('For RDC jobs, use get_specific_real_device_job_asset'), including alternative tools and troubleshooting advice for asset-not-found errors. This comprehensive guidance helps the agent select the correct tool in context.

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