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Stop DataLens Trino cluster

datalens_trinoclusters_stop

Stops a running Trino cluster in DataLens and returns the operation that stops it, so you can shut down cluster resources.

Instructions

Stop a running Trino cluster and return the operation that stops it.

Experimental in the DataLens API, written from its document and never called: not measured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_idYesId of the Trino cluster.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.122.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and openWorldHint=true, covering the safety profile. The description adds genuinely useful context: the call is asynchronous (returns an operation) and is flagged as experimental/never-tested in the DataLens API, which the annotations do not convey. It stops short of permission or failure-mode disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The primary action is front-loaded in the first sentence, with the experimental caveat scoped to a second sentence. Nearly every clause earns its place, though the meta note about tooling ('written from its document and never called') is somewhat verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter asynchronous stop operation with no output schema, the description covers purpose and the operation-return behavior adequately. However, it omits lifecycle context (e.g., what state the cluster ends in, whether stop is reversible, or what the returned operation implies for polling).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With a single parameter and 100% schema description coverage, the schema already documents cluster_id fully. The description adds nothing about parameter meaning, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Stop') and resource ('Trino cluster') and notes the return value (the operation that stops it), so the action is unambiguous. It does not explicitly contrast with siblings like datalens_trinoclusters_start or datalens_trinoclusters_delete, though the distinction is largely obvious from the verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no preconditions, and no mention of alternatives such as delete or start. The agent gets no routing help beyond the obvious name, so usage must be inferred.

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

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