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Python
Remote

Qlty Missingvalues

qlty_missingValues
Read-onlyIdempotent

Identify which columns contain NULL or missing values in a table, returning a column-level summary.

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)

Schema Changelog

Changes observed during successful MCP inspections.

  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. Changed10 schema fields changedv0.2.1
    • 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"
+]
  3. Changed1 schema field changedv1.0.0
    • removedInput schema / title
      Removed value: -"handle_qlty_missingValuesArguments"
  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=true and idempotentHint=true, and the description adds useful context about the optional persist behavior materializing a volatile table and returning a table name. The description does not contradict annotations, and while it could describe the exact output format more fully, it adequately reveals scope and side effects.

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 well-structured and front-loaded: a clear purpose statement, explicit use conditions, and a brief argument list. The argument list duplicates the schema somewhat, which costs a little, but the overall length is still reasonable and every major clause contributes.

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 simple read-only analysis tool, the description covers what it returns, when to use it, when not to use it, and optional persistence behavior. No output schema exists, but the column-level summary and the mention of null counts per column give sufficient expectation; more detail about the exact return structure would be nice but is not critical.

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%, with each parameter already documented in the input schema. The description repeats the parameter meanings without adding material detail beyond what the schema provides, so it meets the baseline but does not elevate comprehension further.

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 containing NULL/missing values in a table and returns a column-level summary. This immediately distinguishes it from related qlty tools that handle negative values, row-level missing data, or statistical summaries.

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 to use this tool when the user asks which columns have nulls or how many nulls exist per column, and explicitly warns against using it to retrieve data rows, directing the agent to qlty_rowsWithMissingValues instead. This is strong routing guidance with a clear alternative.

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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