cloudtrail_lookup_events

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does mention that filters are 'exact, case-sensitive matches and may be combined', which adds useful behavior. However, it does not state whether the operation is read-only, nor does it disclose return format, pagination behavior, or potential side effects. For a lookup tool, this is partially adequate but not comprehensive.

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 two concise sentences with no redundancy. The first sentence states the core function and filters; the second provides a practical use case. It is front-loaded with the primary action and immediately conveys the tool's purpose.

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?

With six optional parameters and no output schema, the description provides the essential context for filtering and use case, but it omits details about the return format, pagination limits, or any prerequisites (e.g., AWS credentials). While the schema covers parameter semantics, the description does not fully compensate for the absence of annotations or output schema, leaving the agent to infer expected behavior.

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 covers all parameters at 100%, so the baseline is 3. The description adds value by explaining that filters are exact, case-sensitive, and combinable, which is not evident from the schema alone. It also gives context on the purpose of the tool, which helps the agent understand parameter usage in a broader sense.

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 verb 'look up' with the specific resource 'AWS CloudTrail management events' and lists the filterable fields (event name, username, resource type). It also provides a concrete use case (auditing who changed what, and from which source IP), which distinguishes it from generic logging tools.

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 gives a clear use case ('auditing who changed what, and from which source IP') that implies when to use it, but it does not explicitly name alternatives or exclusion conditions. While the sibling list includes similar tools like cloudwatch_log_events, the description's focus on CloudTrail and management events makes the intended usage reasonably clear.

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