Skip to main content
Glama
Sign In
Qualtrics MCP Server

Qualtrics MCP Server

by yrvelez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

get_question_template

Read-only

Retrieve a question definition to use as a template for creating new questions, enabling easy replication of complex types like side-by-side or sliders.

Instructions

Get a question's full definition stripped of server-generated fields, ready to reuse as a create_question template (pass it via additionalFields). Best way to replicate complex question types (side-by-side, sliders, heatmaps): build one in the Qualtrics UI, then clone it via the API.

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": {
      "surveyId": "<surveyId>",
      "questionId": "<questionId>"
    },
    "name": "get_question_template"
  }
}

Input Schema

NameRequiredDescriptionDefault
surveyIdYesThe Qualtrics survey ID
questionIdYesThe question ID to use as a template (e.g., QID1)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description adds that it strips server-generated fields, making the tempering of the response transparent. It also explains the output is ready for create_question, providing some behavioral context beyond the annotation.

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 sentences, no filler. The first sentence states the core purpose and output usage, the second provides a best-practice scenario. Efficient and front-loaded.

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?

Given the tool is simple (2 required params, no output schema, no nested objects), the description fully covers what the tool does, what it returns, and how to use the result. It also provides a concrete example, making it complete for agent invocation.

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 clear descriptions for surveyId and questionId. The description does not add additional parameter meaning beyond what the schema provides, so baseline 3 applies.

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 gets a question definition stripped of server-generated fields, specifically for reuse as a create_question template. It also gives a concrete use case (cloning complex question types), distinguishing it from get_question which returns raw definition.

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

Usage Guidelines4/5

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

The description explains the best use case (replicating complex question types via UI then API) and how to use the output (pass via additionalFields). It does not explicitly state when not to use it or name alternatives, but the context is clear enough for the agent to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools