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yrvelez

Qualtrics MCP Server

by yrvelez

add_matrix_question

Add a matrix question to a Qualtrics survey with rows as statements and columns as scale points. Supports Likert and other matrix variants like constant sum, with optional inline text entry on rows.

Instructions

Simplified helper to create a matrix question. Choices = rows/statements, Answers = columns/scale points. Defaults to a Likert single-answer matrix; use selector/subSelector for variants (e.g., selector 'CS' + subSelector 'WOTB' for matrix constant sum). Statements may be objects to enable an inline text-entry box on individual rows (e.g., an 'Other (please specify)' row).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blockIdYesThe block ID to add the question to
selectorNoMatrix selector (default: Likert)
surveyIdYesThe Qualtrics survey ID
statementsYesArray of statement/row labels; pass an object instead of a string to enable per-row inline text entry
scalePointsYesArray of scale point labels (e.g., ['Strongly Disagree', ..., 'Strongly Agree'])
subSelectorNoSub-selector; must match the selector. Likert: SingleAnswer|MultipleAnswer|DL, RO: DND|TX, TE: Short|Medium|Long|Essay, CS: WOTB|WTB (default: SingleAnswer for Likert)
questionTextYesThe question text/instructions
recodeValuesNoNumeric recode mapping for scale points, keyed by answer id, e.g., {"1": "1", "2": "2"}
dataExportTagNoCustom export tag for the question column names (recommended; derived uniquely from questionText if omitted)
forceResponseNoRequire a response for all statements (default: false)
Behavior3/5

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

Annotations are minimal (destructiveHint=false). The description adds behavioral context about default Likert single-answer and inline text entry, but does not disclose permissions, limitations, or side effects beyond what is implied.

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?

Three well-structured sentences with front-loaded purpose. Every sentence adds essential information without redundancy.

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?

Given the complexity (10 params, nested objects) and no output schema, the description covers key aspects (default, variants, inline text) but could mention more parameter behaviors like forceResponse or recodeValues.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying that statements can be objects for inline text and explains the roles of choices and answers, supplementing the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it creates a matrix question and explains terms like statements and scale points. It mentions default Likert and variants via selector/subSelector, but does not explicitly differentiate from siblings like add_likert_question.

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

Usage Guidelines3/5

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

The description implies usage for matrix questions and gives one example of a variant, but lacks explicit guidance on when to use this tool versus alternatives like add_likert_question or add_constant_sum_question.

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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