io.github.Aguantar/clickhouse-dataops-mcp

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ch_data_quality

Identify data quality issues in ClickHouse tables: nulls, duplicate rows, hourly gaps, and missing market coverage for a given date.

Instructions

Run data quality checks: nulls, duplicates, gaps, and market coverage.

Checks for a specific date:

Null/empty values per column
Duplicate rows by primary key
Hourly data gaps (missing time windows)
Market coverage (are all 5 coins present?)
Data freshness

Args: table: Table to check (default: crypto_trades) database: Database name (default: cdc_pipeline) check_date: Date to check in YYYY-MM-DD format (default: today)

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_data_quality"
  }
}

Input Schema

Name	Required	Default
`table`	No	crypto_trades
`database`	No	cdc_pipeline
`check_date`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jun 3, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Lists specific checks performed (nulls, duplicates, gaps, coverage, freshness), which adds behavioral context. However, lacks disclosure on side effects (read-only?), permissions, or rate limits. No annotations provided to compensate.

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?

Concise and well-structured: first lists high-level checks, then provides parameter details. Every sentence is informative without redundancy.

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 purpose and parameters well, but does not describe the output schema or how results are presented. Given output schema exists, this is a minor gap.

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 0% description coverage, but the description's 'Args:' section fully explains each parameter (table, database, check_date) with defaults and purpose, greatly enhancing clarity.

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?

Clearly states it runs data quality checks on a specific date, listing exact checks (nulls, duplicates, gaps, coverage). Distinguishes from sibling tools like ch_query or ch_disk_usage.

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?

Implicitly clear when to use (for data quality checks), but lacks explicit when-not or alternative tools. Still, the specialized nature makes usage obvious.

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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