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Qualtrics MCP Server

Qualtrics MCP Server

by yrvelez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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import_survey_qsf

Create a survey by importing a complete Qualtrics Survey Format (QSF) object using the official multipart import API. Use a QSF exported by the Qualtrics UI.

Instructions

Create a survey from a complete Qualtrics Survey Format (QSF) object using the official multipart import API. Use a QSF exported by the Qualtrics UI; the Survey Version API's format=qsf response is not the same import format.

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": {
      "name": "<name>",
      "qsf": "<qsf>"
    },
    "name": "import_survey_qsf"
  }
}

Input Schema

NameRequiredDescriptionDefault
qsfYesComplete QSF JSON object exported by Qualtrics
nameYesName for the imported survey
filenameNoOptional .qsf filename

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior3/5

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

Annotations only indicate non-destructive behavior. The description adds one behavioral constraint (source of QSF) but omits other details like expected response, error behavior, or idempotency. With limited annotations, more behavioral context would be helpful.

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 wasted words. The critical guidance (format source) is front-loaded, and every sentence serves a purpose.

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 no output schema, the description is adequate for selection. It clarifies input format and constraints. However, it could mention what the tool returns (likely survey ID) or post-creation state to fully complete the usage context.

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 covers all 3 parameters with descriptions. The description adds crucial context: the format for the 'qsf' parameter (must be from UI export, not version API), which goes beyond the schema. No enums or nested parameter details are needed.

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 creates a survey from a QSF object, specifying the exact format and API used. It distinguishes from sibling import tools (import_survey_text, import_survey_from_url, import_survey_docx) by naming the specific QSF format.

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 tells when to use this tool (with QSF exported by Qualtrics UI) and warns against using QSF from the Survey Version API, providing clear alternatives and avoiding misuse.

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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