Ops MCP Server

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it lists all buckets (implying a read-only operation), specifies the return format (creation date, sorted alphabetically), and includes a critical instruction about not guessing/defaulting the 'env' parameter. However, it doesn't mention potential rate limits, authentication needs, or error handling, leaving some gaps.

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 appropriately sized and front-loaded: it starts with the core purpose, follows with usage guidelines, then details parameters and returns. Every sentence adds value without waste, and the structure is clear and efficient for an AI agent to parse.

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 low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is largely complete. It covers purpose, usage, parameters, and behavioral aspects. However, without annotations, it could benefit from more details on authentication or error scenarios, slightly reducing completeness.

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 0% description coverage, so the description must compensate. It adds significant meaning beyond the schema: it explains the 'env' parameter as 'Target environment' with enumerated values ('dev', 'uat', 'test', 'prod') and provides an IMPORTANT note about not guessing/defaulting. This fully compensates for the schema's lack of documentation.

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 specific action ('List all S3 buckets') and resource ('in the AWS account'), distinguishing it from sibling tools like 'browse_s3' or 'list_s3_recursive' which likely operate at different scopes. It precisely defines what the tool does without being vague or tautological.

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 explicitly provides usage guidelines with 'USE THIS TOOL when...' followed by specific scenarios (e.g., user asks about S3 buckets, storage, 'what buckets do we have'), and it includes an important directive to ask the user for the environment if not specified. This offers clear context and exclusions, helping differentiate from alternatives.

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