Teradata MCP Server

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Python

Remote

Qlty Columnsummary

qlty_columnSummary

Read-onlyIdempotent

Get summary statistics for all columns in a table with a single call. Ideal for profiling or overviewing your dataset. Optionally materialize results as a volatile table for further use.

Instructions

Get summary statistics for ALL columns in a table in a single call. Use when the user asks for an overview, profile, or summary of every field in a table. For detailed statistics on a SINGLE specific column (min, max, percentiles), use qlty_univariateStatistics instead.

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_name": "<table_name>"
    },
    "name": "qlty_columnSummary"
  }
}

Input Schema

Name	Required	Description
`persist`	No	If True, materializes result as a volatile table and returns table name
`table_name`	Yes	Table name to analyze
`database_name`	No	Name of the database (optional)

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.1Jul 4, 2026

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)"

Changed10 schema fields changedv0.2.1Apr 30, 2026

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"
+]

Changed1 schema field changedv1.0.0Jan 2, 2026
- removedInput schema / title
```
Removed value: -"handle_qlty_columnSummaryArguments"
```
First observedAug 18, 2025

TDQS

A4.4/5.0

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 the key behavioral detail that it returns summary statistics for ALL columns in a single call, and explains the persist parameter's effect (materializes as a volatile table and returns table name). This adds value 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the core purpose and scope are stated in the first sentence, followed by usage guidance and a brief parameter list. Every sentence earns its place, and the parameter list is a useful quick reference without being verbose.

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 summary tool with 100% schema coverage and no output schema, the description is largely complete. It explains the tool's scope, when to use it, and the persist behavior. The only minor gap is that it doesn't describe the exact structure of the returned summary statistics, but since there is no output schema and the tool is read-only, this is a minor omission.

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 meanings but doesn't add much beyond the schema. The persist behavior is already in the schema description, so the description adds minimal extra semantic value.

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 states a specific verb ('Get summary statistics') and resource ('ALL columns in a table in a single call'), and explicitly distinguishes it from the sibling qlty_univariateStatistics for single-column detailed stats. This makes the tool's purpose immediately clear and differentiates it from similar tools.

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 says when to use this tool ('when the user asks for an overview, profile, or summary of every field in a table') and names the alternative for single-column statistics (qlty_univariateStatistics). This gives clear usage guidance and exclusion criteria.

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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