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io.github.Aguantar/clickhouse-dataops-mcp

io.github.Aguantar/clickhouse-dataops-mcp

by Aguantar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ch_slow_queries

Identifies slow ClickHouse queries and diagnoses root causes such as full scans, memory issues, and aggregation inefficiencies.

Instructions

Find slow queries with root cause diagnosis.

Scans system.query_log for queries exceeding the duration threshold, then generates a diagnosis for each:

  • Full scan detection (high read_rows without partition pruning)

  • Memory-intensive query detection

  • Aggregation optimization suggestions (use pre-aggregated tables)

  • Error detection

Args: hours: Look back period in hours (default: 24) min_duration_ms: Minimum query duration to report (default: 1000ms) limit: Maximum number of slow queries to return (default: 20)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ch_slow_queries"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNo
min_duration_msNo
limitNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description indicates it scans system.query_log (a read operation) and generates diagnoses, but does not explicitly confirm it is read-only or discuss potential side effects, authorization, or performance impact. Adequate but incomplete.

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 a clear title, diagnostic bullet list, and Args section. Slightly verbose; could trim redundant phrasing while preserving info.

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?

Covers main functionality and diagnostics, but omits edge cases, error handling, and output details (though output schema exists). Adequate for the tool complexity.

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

Parameters4/5

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

Input schema has 0% description coverage, but the description's Args section adds meaning for all three parameters (hours, min_duration_ms, limit) with defaults and brief explanations. Lacks full detail on constraints or interactions.

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 finds slow queries with root cause diagnosis, listing specific diagnostic types (full scan, memory, aggregation, error detection). This distinguishes it from siblings like ch_query (general query) and ch_explain_query (plan analysis).

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

Usage Guidelines3/5

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

The description implies use for performance troubleshooting but does not explicitly state when to use it versus alternatives like ch_explain_query or ch_pipeline_latency. No exclusions or specific conditions are mentioned.

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

Deploy Server

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