pod_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, and it does substantial work: it discloses the aggregation semantics (identical events collapsed by reason and message, with count and first/last time, newest first) and the unusual execution path (runs through the Dynatrace Bridge browser extension in the user's logged-in browser) plus failure handling guidance. It stops short of stating the operation is read-only/non-mutating or any permission requirements, which is the remaining gap for a zero-annotation tool.

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?

Three short paragraphs, front-loaded with what the tool returns, then the selection rule, then the environment caveat. No filler sentences, and the most decision-relevant facts (what it lists, which param to pass, what it depends on) come first.

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 7 parameters, zero required, no output schema, and no annotations, the description covers the return shape (grouped events, count, first/last timestamp, ordering, omitted count), the window resolution behavior, the environment default, and points to dynatrace_bridge_status for environment names. An agent has everything needed to call it correctly without guessing.

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?

Schema coverage is 100%, so baseline is 3, but the description adds a real constraint the schema does not encode (required list is empty): exactly one of workload or pod must be passed, and a name matching several entities returns candidates rather than guessing. That mutual-exclusivity rule is the kind of semantics the schema alone would not convey.

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?

States a specific verb and resource ('Lists the Kubernetes events Dynatrace recorded') and then enumerates the concrete event categories (probe failures, container kills/back-offs, scheduling, image pull, mount failures, spec changes). This clearly distinguishes it from siblings like list_events and pod_resources without needing the schema opened.

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?

Gives an explicit selection rule ('Pass exactly one of workload or pod') and a concrete trigger condition ('Use it after pod_resources shows restarts, OOM kills or a pod that is not running, with the window narrowed to that time'), naming the sibling that precedes it. Nothing about when to reach for this rather than alternatives is left to inference.

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