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Teradata MCP Server

Official
by Teradata
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Python
Remote

qlty_rowsWithMissingValues

Read-onlyIdempotent

Retrieve actual data rows where a specific column is NULL or missing. Returns the records themselves for direct inspection.

Instructions

Retrieve the actual data rows where a specific column is NULL or missing. Returns the records themselves, not a column summary. Use when the user wants to SEE or FETCH the rows with missing values in a named column. Do NOT write a SQL query with base_readQuery for this — always use this tool when the request is about rows with null values. Do NOT use for a column-level summary of which columns have nulls — use qlty_missingValues for that.

Arguments: database_name - Name of the database (optional) table_name - Table name to analyze column_name - Column name to analyze for missing values 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 for missing values
database_nameNoName of the database (optional)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  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 for missing values"
    • 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_rowsWithMissingValuesArguments"
  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 and idempotentHint, so the tool is known safe. The description adds value by clarifying it returns actual rows (not summaries) and that the persist parameter materializes results as a volatile table, which is useful behavioral context.

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 front-loaded with the core purpose and usage guidelines. It is concise but includes a redundant argument list that echoes the schema; while not verbose, it could be slightly tighter.

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?

Given no output schema and 4 parameters, the description covers the tool's return type (rows), persist behavior, and usage context. It does not explicitly detail the output format when persist is false, but the overall picture is complete for common use.

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%; all parameters are documented in the input schema. The description repeats this information in a bullet list without adding new semantics beyond the schema, meeting the baseline for high coverage.

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 retrieves actual rows with missing values in a specified column. It explicitly contrasts with sibling tools base_readQuery and qlty_missingValues, making the tool's unique purpose unmistakable.

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 provides explicit when-to-use (user wants to see/fetch rows with null values) and when-not-to-use (do not write SQL, do not use for column summary). It names specific alternatives (base_readQuery, qlty_missingValues), leaving no ambiguity.

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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