Create DataLens Spark application

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

Annotations already declare readOnly=false, openWorld=true, idempotent=false, destructive=false, so the safety profile is covered. The description then adds genuinely new behavioral context: the endpoint is experimental, written only from documentation, never exercised, and unmeasured, which is exactly the kind of reliability warning annotations cannot express. It still omits auth requirements, rate limits, and whether the returned operation must be polled.

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?

Two short sentences, purpose front-loaded and the caution immediately after; nothing is padded. The phrasing 'the operation that makes it' is slightly awkward, and the second sentence could be tightened, but it is well within a reasonable size.

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 single-body-parameter creation tool with annotations providing the safety profile, the description covers purpose, return type and an important experimental caveat. It doesn't mention that the three application variants are mutually exclusive or that the returned operation is asynchronous, but those gaps are largely filled by the schema.

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% and the body parameter already documents the clusterId/name/catalogs plus the exactly-one-of sparkApplication/pysparkApplication/sparkConnectApplication structure. The description adds no parameter-level meaning beyond 'on a cluster', so the baseline 3 for schema-driven params applies.

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 ('Make a Spark application on a cluster') and adds the return type ('the operation that makes it'), which naturally separates it from the sibling list/get/cancel/log_list tools. The verb 'make' is less precise than 'create', and no sibling is named explicitly, so it lands just short of a 5.

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?

It gives a real usage instruction ('Ask the person before calling') tied to the tool's experimental status, which is useful. However it never names an alternative tool or spells out when this is preferred over, say, datalens_sparkclusters_create or the other sparkapplications tools, so the when-vs-alternatives guidance is only implied.

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