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Teradata MCP Server

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by Teradata
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Python
Remote

sql_Analyze_Cluster_Stats

Read-onlyIdempotent

Analyze pre-computed SQL query cluster statistics to identify performance optimization opportunities, rank resource consumers, and detect skew problems for targeted tuning.

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

A5/5.0
Behavior5/5

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

Annotations already mark it as readOnly=true and idempotent=true. The description adds valuable context: it analyzes pre-computed stats without re-running the pipeline, lists analysis capabilities, and explains output format. No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with headings, bullet points, and code blocks. Front-loaded with purpose, then detailed sections on capabilities, metrics, and usage. Every sentence adds value; no redundant or filler content. Appropriate length for the complexity.

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?

Given no output schema, the description thoroughly covers inputs (sorting metrics, limit), behavior (analysis of pre-computed stats), and output format (detailed cluster statistics). It also provides categorization, workflow, and decision framework. Complete for an analysis tool.

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 has 2 params with 0% description coverage. The description compensates fully by listing all available sorting metrics with explanations (e.g., 'avg_cpu: Average CPU seconds'), describing the default behavior, and explaining limit_results usage. Adds significant meaning beyond 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 for optimization, with a specific verb ('analyzes') and resource ('cluster stats'). It distinguishes itself from sibling tools like sql_Execute_Full_Pipeline and sql_Retrieve_Cluster_Queries by focusing on analysis of existing data.

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

Usage Guidelines5/5

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

Provides explicit usage guidance including a typical workflow (sort by metrics, use limit_results), an optimization decision framework (e.g., High CPU + High Query Count = max impact candidates), and how to prioritize. Clearly differentiates from sibling tools by focusing on analysis rather than execution or query retrieval.

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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