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sql_Analyze_Cluster_Stats

Analyze pre-computed SQL query cluster statistics to identify optimization opportunities. Rank clusters by CPU, I/O, skew, or complexity to pinpoint high-impact candidates and improve workload performance.

Instructions

ANALYZE SQL QUERY CLUSTER PERFORMANCE STATISTICS

This tool analyzes pre-computed cluster statistics to identify optimization opportunities without re-running the clustering pipeline. Perfect for iterative analysis and decision-making on which query clusters to focus optimization efforts.

ANALYSIS CAPABILITIES:

  • Performance Ranking: Sort clusters by any performance metric to identify top resource consumers

  • Resource Impact Assessment: Compare clusters by CPU usage, I/O volume, and execution complexity

  • Skew Problem Detection: Identify clusters with CPU or I/O distribution issues

  • Workload Characterization: Understand query patterns by user, application, and workload type

  • Optimization Prioritization: Focus on clusters with highest impact potential

AVAILABLE SORTING METRICS:

  • avg_cpu: Average CPU seconds per cluster (primary optimization target)

  • avg_io: Average logical I/O operations (scan intensity indicator)

  • avg_cpuskw: Average CPU skew (distribution problem indicator)

  • avg_ioskw: Average I/O skew (hot spot indicator)

  • avg_pji: Average Physical-to-Logical I/O ratio (compute intensity)

  • avg_uii: Average Unit I/O Intensity (I/O efficiency)

  • avg_numsteps: Average query plan complexity

  • queries: Number of queries in cluster (frequency indicator)

  • cluster_silhouette_score: Clustering quality measure

PERFORMANCE CATEGORIZATION: Automatically categorizes clusters using configurable thresholds (from sql_opt_config.yml):

  • HIGH_CPU_USAGE: Average CPU > config.performance_thresholds.cpu.high

  • HIGH_IO_USAGE: Average I/O > config.performance_thresholds.io.high

  • HIGH_CPU_SKEW: CPU skew > config.performance_thresholds.skew.high

  • HIGH_IO_SKEW: I/O skew > config.performance_thresholds.skew.high

  • NORMAL: Clusters within configured normal performance ranges

TYPICAL ANALYSIS WORKFLOW:

  1. Sort by 'avg_cpu' or 'avg_io' to find highest resource consumers

  2. Sort by 'avg_cpuskw' or 'avg_ioskw' to find distribution problems

  3. Use limit_results to focus on top problematic clusters

OPTIMIZATION DECISION FRAMEWORK:

  • High CPU + High Query Count: Maximum impact optimization candidates

  • High Skew + Moderate CPU: Distribution/statistics problems

  • High I/O + Low PJI: Potential indexing opportunities

  • High NumSteps: Complex query rewriting candidates

OUTPUT FORMAT: Returns detailed cluster statistics with performance rankings, categories, and metadata for LLM analysis and optimization recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sort_by_metricNoavg_cpu
limit_resultsNo

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

Without annotations, the description carries the full burden. It implies a read-only operation by stating it analyzes pre-computed statistics without re-running pipelines. It details output contents, but does not explicitly declare no side effects or authentication needs. The description is sufficiently transparent for a read-analysis tool.

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 description is well-structured with clear headings and sections, front-loading the purpose. While lengthy, the content is relevant and earns its place. Minor verbosity could be trimmed, but overall it is organized and concise for the complexity.

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

Completeness4/5

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

The description covers input parameters thoroughly and explains the output as 'detailed cluster statistics with performance rankings, categories, and metadata.' With no output schema, the description provides adequate context for an LLM, though more specific output format details could enhance completeness.

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

Parameters5/5

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

The input schema has zero description coverage (0%), but the tool description compensates thoroughly: it lists nine sorting metrics with meanings for sort_by_metric and explains limit_results usage in the workflow. This adds significant semantic value beyond the schema's bare parameter names.

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

Purpose5/5

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

The description clearly states the tool analyzes SQL query cluster performance statistics using pre-computed data, with specific capabilities like performance ranking, resource impact assessment, and skew detection. It distinguishes from related siblings by noting it does not re-run the clustering pipeline, making its purpose distinct from sql_Execute_Full_Pipeline.

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

Usage Guidelines4/5

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

The description provides a typical analysis workflow and decision framework, guiding when to sort by different metrics. However, it does not explicitly state when not to use this tool or directly compare to siblings like sql_Retrieve_Cluster_Queries, leaving some ambiguity.

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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