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data_quality_report

Generate a data quality report for any database table, checking completeness, detecting duplicate records, and validating consistency to identify data issues before analysis.

Instructions

Комплексный отчёт о качестве: полнота, дубликаты, консистентность.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_name": "<table_name>"
    },
    "name": "data_quality_report"
  }
}

Input Schema

Name	Required	Description	Default
`table_name`	Yes
`key_columns`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations, the description carries the burden and does disclose the general behavior: it reports on completeness, duplicates, and consistency. However, it does not clarify whether the operation is read-only, how key_columns influences the report, or what execution behavior to expect, leaving the agent to infer these details.

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?

The description is a single front-loaded sentence with no filler. Every word contributes to communicating the tool's core output and scope, and it is appropriately compact for a simple report tool.

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

Completeness2/5

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

Although an output schema exists and return values need not be described, the definition is incomplete for practical selection and invocation: it lacks usage guidance, parameter explanation, and enough behavioral detail. An agent given this text among 40+ siblings would have difficulty deciding when to use it and how to set key_columns.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it does not explain either parameter. 'key_columns' is left entirely undefined, and the description never connects it to the duplicate/consistency checks despite the name suggesting that relationship.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb-like resource: it produces a comprehensive quality report covering completeness, duplicates, and consistency. It goes beyond a tautology by enumerating concrete quality dimensions, though it does not explicitly distinguish itself from siblings like find_duplicates.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus alternatives such as profile_data, find_duplicates, or detect_anomalies. The intended use is only implied by the name and phrase 'quality report'; no exclusions or alternative conditions are provided.

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