datalens_sparkclusters_resource_presets_getRetrieve a DataLens Spark cluster resource preset's cores and memory by preset ID and cloud environment ID.
One resource preset: its cores and its memory. Experimental API, not measured.
| Name | Required | Description | Default |
|---|---|---|---|
| resource_preset_id | Yes | Resource preset id. | |
| cloud_environment_id | Yes | The cloud environment the presets are for. |
Changes observed during successful MCP inspections.
v0.122.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile needs no restating. The description does add something beyond the structured fields by flagging the API as experimental and 'not measured', but this warning is cryptic and never explains what the caveat means practically.
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 terse sentences with the resource described first and the experimental caveat last; nothing is padded. Very short, but each clause carries content.
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-param read that has no output schema, the description helpfully names the returned fields and warns of experimental status. It nonetheless omits how the cloud_environment_id scopes the lookup and offers no pagination/format detail, leaving it minimally adequate.
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 both required params (resource_preset_id, cloud_environment_id) are already documented in the schema. The description adds no parameter-level detail, so the baseline of 3 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 ('One resource preset') and even lists the returned attributes (cores, memory), which implicitly separates it from the sibling datalens_sparkclusters_resource_presets_list. It does not name or contrast against that sibling explicitly, keeping it from 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?
No when-to-use guidance and no mention of the obviously related datalens_sparkclusters_resource_presets_list alternative. The only contextual note is 'Experimental API, not measured', which is a caveat rather than routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.