investigate_datastore

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds real context beyond them: reads are batched, returns a correlated bundle rather than the list envelope, excludes per-datastore latency, is a point-in-time snapshot with no trending, and unknown names yield a teaching error.

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?

Front-loaded scope statement, then alternative-routing, then bundle contents, then boundaries — every sentence earns its place and there is no filler. Length is justified by the composite return.

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 no output schema, the description fully enumerates the returned bundle (capacity/free space/accessibility, mounting hosts, VM rollup, alarms, merged event timeline) and states what is excluded, so an agent knows exactly what it gets back.

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 100%, so hours, target, and datastore_name are already documented structurally; the description adds only the provenance hint for datastore_name (from list_all_datastores or get_datastore_capacity). Baseline 3 is appropriate where the schema carries the parameter burden.

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?

Specific verb (investigate) plus resource (datastore), and it explicitly frames the tool as a correlated drill-down that replaces stitching four named sibling tools together. An agent can distinguish this from list_all_datastores, get_datastore_capacity, get_alarms, and get_events without opening any schema.

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?

Explicit when-to-use ('Reach for it after get_cluster_health_summary flags storage pressure'), explicit alternative-avoidance ('instead of stitching ... yourself'), and explicit when-not / hand-off ('Freeing space ... belongs to vmware-aiops'). Nothing 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.