s3_list_buckets
List all S3 buckets in your AWS account to get a complete inventory of storage resources.
Instructions
List all S3 buckets in the AWS account
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List all S3 buckets in your AWS account to get a complete inventory of storage resources.
List all S3 buckets in the AWS account
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description alone must disclose behavioral traits. It only states the operation without mentioning required permissions (e.g., s3:ListAllMyBuckets), potential pagination, return value structure, or whether it is read-only. This leaves significant behavioral ambiguity for an AI agent.
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 a single, front-loaded sentence that communicates the exact operation without any redundant words. It is appropriately sized for a tool with no parameters, and every word earns its place. This is a model of conciseness.
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 simplicity (no parameters, no output schema, no annotations), the description states the core action but omits non-obvious context such as required IAM permissions, the exact return format (list of bucket names?), and when to prefer this over s3_list_objects. It is minimally complete but leaves gaps that could affect an agent's ability to use it confidently.
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 tool has zero parameters, and the schema confirms this with an empty properties object. Since there are no parameters to document, the description bears no responsibility for parameter semantics. The baseline of 4 applies because the absence of parameters makes the tool trivially simple in this dimension.
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 action ('List') and resource ('all S3 buckets in the AWS account'), making it unmistakable and differentiating it from sibling tools like s3_list_objects (which targets objects within a bucket) and s3_presign_get (which presigns a URL). The scope is precise and the verb is action-oriented.
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 no guidance on when to use this tool versus the sibling tools. It doesn't mention scenarios like enumerating all buckets before selecting one, nor does it exclude alternatives. The purpose is inferable from the name, but explicit usage context is entirely missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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