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BachStudio Teradata MCP Server

by BACH-AI-Tools
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Python
Remote

sql_Analyze_Cluster_Stats

Analyze pre-computed query cluster statistics to identify performance optimization opportunities and prioritize tuning efforts.

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

A4.5/5.0
Behavior4/5

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

No annotations provided, but the description fully discloses the tool's read-only analysis behavior, listing capabilities, metrics, and categorization logic. It does not mention side effects or required permissions but is transparent about its non-destructive nature.

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?

Well-structured with sections and bullet points, but somewhat verbose. The first paragraph is concise; subsequent sections add detailed guidance. Could be trimmed without losing value, but organization aids readability.

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

Completeness5/5

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

Comprehensive coverage for a real-only analysis tool: explains what it does, how to use, output format, and optimization decision framework. No output schema, but the description adequately sets expectations for the returned data.

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 by explaining both parameters: sort_by_metric with a full list of available metrics and their meanings, and limit_results as a refinement. This adds critical context 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's purpose: analyzing pre-computed cluster statistics for optimization opportunities, with specific verbs like 'analyze' and 'identify'. It differentiates from siblings like sql_Execute_Full_Pipeline by noting it avoids re-running the clustering 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?

Provides explicit workflow steps (sort by metrics, limit results) and a decision framework for optimization. However, it lacks explicit exclusions or direct comparisons to alternatives, though the context implies when to use it.

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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