sql_Analyze_Cluster_Stats

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

Annotations indicate read-only and idempotent behavior. The description reinforces this by stating it analyzes pre-computed statistics and describes the output format. It adds context about performance categorization and thresholds, which goes beyond 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 well-structured with sections and bullet points, but it is lengthy. While detailed, some parts like the optimization decision framework could be condensed. It is adequate but not maximally concise.

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 complexity (2 parameters, no output schema, annotations present), the description explains the tool's purpose, available metrics, usage workflow, and output format. It compensates for missing schema descriptions and provides sufficient context for an AI agent.

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 schema has 2 parameters with 0% description coverage. The description lists available sorting metrics for sort_by_metric and mentions using limit_results to focus on top clusters, providing necessary meaning for both parameters.

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 analyzes pre-computed cluster statistics to identify optimization opportunities without re-running the pipeline. It lists specific analysis capabilities and differentiates itself from sibling tools like sql_Execute_Full_Pipeline by emphasizing iterative analysis.

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 provides a typical analysis workflow and optimization decision framework, guiding when to use the tool (e.g., for iterative analysis) and what steps to take. It implicitly contrasts with other tools but lacks explicit exclusion criteria for when not to use it.

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