oci_kafka_recommend_scaling

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

No annotations provided, so description carries full burden. It clearly describes the tool as non-destructive data collection and specifies output structure (diagnostic report sections). No contradictions.

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?

Description is concise and well-structured: a single sentence summary, bulleted data collection points, and a detailed list of report contents. Every sentence adds value; 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?

Given zero parameters and existence of output schema, the description fully explains the tool's purpose, inputs (implicit cluster context), outputs, and how the agent should use the results. Covers all necessary context.

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?

Input schema has zero parameters, so no parameter descriptions are needed. Schema coverage is 100% vacuously. Description explains tool behavior without parameter details, meeting the baseline of 4 for zero-parameter tools.

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 states 'Analyze the cluster and recommend scaling actions' and lists specific data collected. It clearly distinguishes from sibling tools like oci_kafka_scale_cluster (which performs scaling) by emphasizing it gathers data only.

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?

Explicitly states 'This tool gathers data only — the LLM agent should interpret the findings and present human-readable recommendations to the user.' This provides clear guidance on when to use the tool (analysis) and when not to present raw output, and implies alternative tools (scale cluster).

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