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Teradata MCP Server

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by Teradata

qlty_rowsWithMissingValues

Extract rows with null or missing values in a given column from a Teradata table, with option to save as volatile table.

Instructions

Get the rows that have missing values in a specific column of a table.

Arguments: database_name - Name of the database (optional, omit if table_name is fully qualified) 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
table_nameYesTable name to analyze
column_nameYesColumn name to analyze for missing values
persistNoIf True, materializes result as a volatile table and returns table name
database_nameNoName of the database (optional, omit if table_name is fully qualified)
Behavior3/5

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

Discloses the persist behavior (materializing as a volatile table) but omits details on default return format, limits, or safety implications; no annotations present to compensate.

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?

Description is concise with a clear opening sentence and structured argument list; minor improvement possible with formal bullet points.

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

Completeness2/5

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

Missing output schema and no description of return format for non-persist case; incomplete for an agent to anticipate behavior fully.

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 parameter descriptions; the description merely repeats them, adding no extra semantic value beyond the schema.

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 retrieves rows with missing values in a specific column, distinguishing it from sibling tools like qlty_missingValues which likely summarize counts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus related tools; lacks explicit when-to-use or when-not-to-use context.

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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