get_databricks_cluster_efficiency

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

With no annotations, the description carries the full burden and adequately discloses that the tool performs read-only checks (audit) and returns a prioritized list of issues and estimated wasted spend. It does not mention authentication, rate limits, or potential failures, but for an audit tool, these are less critical and the behavioral traits are clear.

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 concise (under 150 words), uses bullet points for readability, and front-loads the main purpose. Every sentence adds value with no redundancy.

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 audit tool with no output schema, the description sufficiently explains the return (prioritized list of issues and estimated wasted spend). It covers the complexity fully, assuming no additional behavioral details are necessary.

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?

The input schema has zero parameters, so the description must provide all meaning. It does so by detailing the specific efficiency checks and output format, adding value beyond the empty schema. Baseline 4 is appropriate.

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 the tool audits Databricks clusters for efficiency issues and cost waste, listing specific checks like missing auto-termination and idle clusters. It is a specific verb+resource (audit all Databricks clusters) and distinguishes from sibling tools that target other platforms or services (e.g., audit_aws_waste, audit_gcp_waste).

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 implies usage for Databricks cluster efficiency audits but does not explicitly state when to use this tool versus alternatives like get_databricks_costs or get_databricks_dbu_breakdown. It lacks exclusions or guidance on when not to use it, relying on the tool name and sibling context to differentiate.

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