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manzoor-source

Teradata MCP Server

qlty_missingValues

Read-onlyIdempotent

Analyze a table to list columns with NULL or missing values, providing a column-level summary of missing data.

Instructions

List the column names that contain NULL or missing values in a table. Returns a column-level summary showing WHICH columns have missing data. Use when the user asks which columns have nulls, which fields have missing data, or how many nulls exist per column. Do NOT use to retrieve the actual data rows — use qlty_rowsWithMissingValues to get the specific records where a column is null.

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)
Behavior4/5

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

Annotations indicate read-only and idempotent behavior. Description adds that it returns a column-level summary and explains the persist parameter's effect, adding value beyond annotations.

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?

Description is concise, front-loaded with purpose, and well-structured with usage guidelines and parameter list. No unnecessary information.

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

Completeness5/5

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

Given 3 parameters, no output schema, and rich annotations, the description provides complete guidance including usage context, parameter explanations, and return value nature. Adequate for an AI agent.

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% with descriptions for each parameter. The description repeats parameter info but does not add meaning beyond the schema, so baseline score of 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 the tool lists column names with NULL/missing values and returns a column-level summary. It distinguishes from the sibling tool qlty_rowsWithMissingValues.

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 tells when to use (user asks for columns with nulls) and when not to (actual data rows), providing a direct alternative (qlty_rowsWithMissingValues).

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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