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Remote

Qlty Univariatestatistics

qlty_univariateStatistics
Read-onlyIdempotent

Calculate full univariate statistics for a single numeric column, returning min, max, mean, standard deviation, quartiles, and percentiles.

Instructions

Calculate full univariate statistics for a single numeric column including min, max, mean, standard deviation, quartiles, and percentiles. Use when the user asks for a complete or comprehensive statistical breakdown of one specific column. For just mean and standard deviation, use qlty_standardDeviation. For statistics across ALL columns in a table at once, use qlty_columnSummary.

Arguments: database_name - Name of the database (optional) table_name - Table name to analyze column_name - Column 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
column_nameYesColumn 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. Changed12 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / column_name / description
      Added value: +"Column name to analyze"
    • removedInput schema / properties / column_name / title
      Removed value: -"Column Name"
    • 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",
-  "column_name"
-]New value: +[
+  "table_name",
+  "column_name"
+]
  3. Changed4 schema fields changedv1.0.0
    • removedInput schema / properties / col_name
      Removed value: -{
-  "title": "Col Name",
-  "type": "string"
-}
    • addedInput schema / properties / column_name
      Added value: +{
+  "title": "Column Name",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "database_name",
-  "table_name",
-  "col_name"
-]New value: +[
+  "database_name",
+  "table_name",
+  "column_name"
+]
    • removedInput schema / title
      Removed value: -"handle_qlty_univariateStatisticsArguments"
  4. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds useful context about the persist option materializing a volatile table and returning a table name. The only minor omission is the return behavior for the ordinary non-persist case, but this is not severe given the annotation coverage.

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?

The description is well-structured and front-loads the core purpose before alternatives. It includes a mini parameter list, which is slightly redundant with the schema, but the text is dense and every part adds usable context.

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

Completeness3/5

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

The tool lacks an output schema, so the description should clarify what the agent can expect when persist is false. It only mentions the returned table name when persist is true, leaving the normal result shape implicit. Otherwise, all key usage and routing information is present.

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 description coverage is 100%, so the schema already explains all four parameters. The description repeats the same parameter descriptions without adding deeper semantics, such as formatting expectations or constraints on numeric columns, so it stays at the baseline for fully covered schemas.

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 states a specific action, 'Calculate full univariate statistics for a single numeric column', and enumerates the outputs (min, max, mean, standard deviation, quartiles, percentiles). It also explicitly differentiates itself from qlty_standardDeviation and qlty_columnSummary, making its scope immediately clear.

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?

It gives a direct 'Use when' condition: when the user asks for a complete or comprehensive statistical breakdown of one specific column. It also names concrete alternatives with their selection criteria, such as using qlty_standardDeviation for just mean/std and qlty_columnSummary for all columns.

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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