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Python
Remote

qlty_columnSummary

Read-onlyIdempotent

Get summary statistics for every column in a table in a single query. Ideal for profiling or overviewing all fields.

Instructions

Get summary statistics for ALL columns in a table in a single call. Use when the user asks for an overview, profile, or summary of every field in a table. For detailed statistics on a SINGLE specific column (min, max, percentiles), use qlty_univariateStatistics instead.

Arguments: database_name - Name of the database (optional) table_name - Table name to analyze persist - If True, materializes result as a volatile table and returns table name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
persistNoIf True, materializes result as a volatile table and returns table name
table_nameYesTable name to analyze
database_nameNoName of the database (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.1
    • changedInput schema / properties / database_name / description
      Previous value: -"Name of the database (optional, omit if table_name is fully qualified)"New value: +"Name of the database (optional)"
  2. Changed10 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / database_name / anyOf
      Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
    • addedInput schema / properties / database_name / default
      Added value: +""
    • addedInput schema / properties / database_name / description
      Added value: +"Name of the database (optional, omit if table_name is fully qualified)"
    • removedInput schema / properties / database_name / title
      Removed value: -"Database Name"
    • addedInput schema / properties / database_name / type
      Added value: +"string"
    • addedInput schema / properties / persist
      Added value: +{
+  "default": false,
+  "description": "If True, materializes result as a volatile table and returns table name",
+  "type": "boolean"
+}
    • addedInput schema / properties / table_name / description
      Added value: +"Table name to analyze"
    • removedInput schema / properties / table_name / title
      Removed value: -"Table Name"
    • changedInput schema / required
      Previous value: -[
-  "database_name",
-  "table_name"
-]New value: +[
+  "table_name"
+]
  3. Changed1 schema field changedv1.0.0
    • removedInput schema / title
      Removed value: -"handle_qlty_columnSummaryArguments"
  4. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral context about the persist parameter (materializes as volatile table and returns table name if True). No contradictions. Could clarify default return format (data vs table name).

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?

Three sentences for purpose and usage, plus a bullet list for parameters. No unnecessary words. Front-loaded with core action. Highly efficient.

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?

No output schema, but description indicates return of 'summary statistics' and optionally a table name. This is sufficient for an overview tool. Could detail what statistics are included, but not essential given sibling differentiation.

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

Parameters3/5

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

Schema coverage is 100%, so description's role is limited. The description restates parameter purposes in a bullet list, adding the persist behavior context but not much beyond the schema. Baseline 3 is appropriate.

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 it returns summary statistics for ALL columns in a table, distinguishing it from the sibling tool qlty_univariateStatistics for single columns. The verb 'get' and resource 'summary statistics for all columns' are specific.

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?

Explicitly advises when to use ('when user asks for overview, profile, or summary of every field') and when not to use ('for detailed statistics on a SINGLE specific column, use qlty_univariateStatistics instead'). This provides clear context for selection.

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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