clusters_create

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

Annotations already indicate this is a write operation (readOnlyHint=false). The description adds that the tool returns cluster_id on success, which is useful. It does not disclose potential costs, time to create, or failure modes, but these are less critical given the annotations.

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 three sentences, front-loading the core purpose and return value. Every sentence serves a purpose: stating the function, the return value, and cross-referencing helper tools.

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?

Given the tool's complexity (19 parameters, many optional), the description is insufficient. It does not explain the purpose of optional parameters, nor does it mention the output schema structure beyond returning cluster_id. The agent would need to infer many details from the incomplete parameter descriptions.

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 only 37%, so the description should compensate, but it only discusses two parameters (spark_version and node_type_id). Many parameters (e.g., num_workers, autotermination_minutes, cloud attributes) lack any explanatory context in the description, leaving the agent under-informed.

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 clearly states that the tool creates a Databricks cluster and returns the cluster_id. It also mentions related tools for discovering valid parameter values, which helps differentiate from sibling tools like clusters_edit or clusters_delete.

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 instructs users to use clusters_spark_versions and clusters_list_node_types to find valid values for spark_version and node_type_id. This is a helpful guideline. However, it does not mention when to avoid this tool (e.g., for editing an existing cluster) or prerequisites like workspace permissions.

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