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Sql Analyze Cluster Stats

sql_Analyze_Cluster_Stats
Idempotent

Analyzes pre-computed query cluster statistics to rank resource consumers, detect CPU/I/O skew, and prioritize optimization targets by performance metrics.

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
limit_resultsNo
sort_by_metricNoavg_cpu

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / limit_results / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "null"
+  }
+]
    • removedInput schema / properties / limit_results / title
      Removed value: -"Limit Results"
    • removedInput schema / properties / limit_results / type
      Removed value: -"integer"
    • removedInput schema / properties / sort_by_metric / title
      Removed value: -"Sort By Metric"
  2. Addedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations provide idempotentHint=true but no read-only guarantee. The description adds useful behavioral context: it consumes pre-computed statistics, does not re-run clustering, applies thresholds from sql_opt_config.yml, and returns ranked/categorized results. No contradiction with annotations.

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 long but well-organized with clear sections, bullet lists, and front-loaded purpose. Some redundancy and a missing step in the workflow list slightly reduce polish, but the structure makes the content scannable and useful.

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?

For a tool with no output schema, the description covers metrics, categorization, decision frameworks, and output format. It could be more explicit about exact return fields and preconditions, but an agent has enough information to invoke it correctly.

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?

Schema description coverage is 0%, but the description compensates thoroughly by listing every valid sort_by_metric value with its meaning and giving limit_results a practical role in focusing on top clusters. This is far beyond the bare schema.

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 pre-computed cluster statistics to identify optimization opportunities without re-running the clustering pipeline.' This specifies a distinct verb, resource, and scope, and differentiates it from pipeline-execution and query-retrieval siblings.

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?

It positions the tool as 'Perfect for iterative analysis and decision-making' and provides a typical workflow with sorting and limit_results. It does not explicitly name alternative tools or state when not to use it, but the context is clear enough.

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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