find_dynatrace_entity

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does reveal partial-name matching and optional type filtering, and the verb 'Look up' implies a read-only operation. However, it does not describe return cardinality, multiple-match behavior, or failure modes, which are relevant for a partial-match lookup.

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 sentences with the operation and options front-loaded, followed by the use-case context. There is no redundant wording or filler, and every sentence contributes to understanding.

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?

For a simple two-parameter lookup, the description covers the purpose, input semantics, and the connection to `correlate_trace`. An output schema exists, so return-value documentation is already handled. The only minor gap is explicit workflow guidance tying it to siblings like `list_pods`, but the description is still sufficiently complete.

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 description coverage is 0%, so the description must compensate. It explains that `name` is a partial display name and that `entity_type` is an optional filter, providing concrete examples like 'CLOUD_APPLICATION' and 'KUBERNETES_CLUSTER'. This adds meaning beyond the bare schema, though it omits minor details like case sensitivity or default filter behavior.

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 states a specific verb ('Look up'), resource ('Dynatrace entity IDs'), and matching behavior ('by (partial) display name'). It also names the downstream consumer ('correlate_trace needs'), which clearly distinguishes it from the Kubernetes/ArgoCD sibling 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 explicitly says 'Use this to bridge a Kubernetes pod/workload name to the Dynatrace entity ID that correlate_trace needs,' giving a clear context for when this tool is appropriate. It does not mention exclusions or alternative lookup methods, but the stated use case is sufficient for an agent to route correctly.

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