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Teradata MCP Server

Official
by Teradata
OverviewSchemaRelated ServersScoreDiscussions(1)
Python
Remote

qlty_standardDeviation

Read-onlyIdempotent

Calculate the mean and standard deviation for a single numeric column in a Teradata table to measure variability and spread of values.

Instructions

Calculate the mean (average) and standard deviation for a single numeric column. Use when the user asks specifically for standard deviation, the spread of values, or just mean and variability. For a fuller statistical profile including min, max, quartiles, and percentiles, use qlty_univariateStatistics instead.

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. 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"
    • 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_standardDeviationArguments"
  4. First observed

TDQS

A4.5/5.0
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 read-only and idempotent. Description adds that it calculates mean and stddev, and explains the persist argument (materializes result as volatile table). No contradictions.

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?

Two clear sentences followed by a concise argument list. Front-loaded purpose, no wasted words. Every sentence earns its place.

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?

For a simple tool with annotations and no output schema, description covers behavior, usage, parameters, and alternatives. Complete and sufficient for agent to select and invoke correctly.

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. Description repeats parameter info, adding no new semantic meaning beyond what 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?

Description states it calculates mean and standard deviation for a single numeric column, with specific verb 'Calculate'. It distinguishes from sibling tool qlty_univariateStatistics, which provides a fuller profile.

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 says when to use: 'when the user asks specifically for standard deviation, the spread of values, or just mean and variability.' Also provides alternative: 'For a fuller statistical profile... use qlty_univariateStatistics instead.'

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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