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Python
Remote

Qlty Negativevalues

qlty_negativeValues
Read-onlyIdempotent

Identify numeric columns with negative values in a Teradata table, flagging entries below zero. Returns the list of affected column names.

Instructions

Identify which numeric columns in a table contain negative values. Use when the user asks about negative numbers, values below zero, or columns with anomalous negative entries. Returns the list of affected column names.

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_negativeValuesArguments"
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds behavioral context by noting that it returns the list of affected column names, and clarifies the persist option materializes a volatile table. This is meaningful beyond the annotations.

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 concise, front-loaded with the main purpose, and then lists parameters. The second sentence could be considered slightly repetitive with the schema, but overall it's compact and well-organized.

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?

For a read-only analysis tool with full schema coverage and no output schema, the description is fairly complete. It states what it returns (list of affected column names) and when to use it. It doesn't mention edge cases like tables with no numeric columns, but that's a minor gap given the tool's simplicity.

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 documents all three parameters. The description repeats the parameter names and brief hints but adds little beyond what the schema provides. 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 identifies what the tool does: it identifies which numeric columns in a table contain negative values. It distinguishes itself from siblings like qlty_missingValues and qlty_distinctCategories by focusing specifically on negative values, and it even includes usage examples ('negative numbers, values below zero'). This is a specific verb+resource combination.

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?

The description explicitly states when to use this tool: 'Use when the user asks about negative numbers, values below zero, or columns with anomalous negative entries.' Although it doesn't name sibling alternatives, among the qlty_* siblings this is clearly the negative-value-specific one. It gives 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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