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Qualtrics MCP Server

Qualtrics MCP Server

by yrvelez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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update_response

Update embedded data fields on an existing survey response. Use this to modify custom data without altering answer values.

Instructions

Start a Qualtrics job to update embedded data on one existing survey response. The public API does not support rewriting recorded answer values through this operation; use create_response or response import for answer data.

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>",
      "responseId": "<responseId>",
      "embeddedData": "<embeddedData>"
    },
    "name": "update_response"
  }
}

Input Schema

NameRequiredDescriptionDefault
surveyIdYesThe Qualtrics survey ID
responseIdYesThe response ID to update
embeddedDataYesEmbedded-data fields to update; Qualtrics requires string values of at most 1000 characters
resetRecordedDateNoReset the response recorded date when the job runs (default: false)

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?

With annotations only having destructiveHint=false, the description adds context about the tool's limitations (cannot rewrite answers) and that it updates only embedded data. However, it does not disclose job behavior, error conditions, or the nature of the operation beyond being a 'job'. Moderate transparency beyond 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?

Two sentences with no waste: first sentence defines purpose and resource, second sentence clarifies limitations and alternatives. Perfectly concise and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the primary function and limitations but omits details about the job mechanism (e.g., what the job returns, whether it's asynchronous) and does not mention the resetRecordedDate parameter. Given no output schema, some context is missing. Adequate but not fully complete.

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 parameters well. The description adds minimal extra meaning, only clarifying that the tool updates embedded data (not answers), which is already implied. Baseline of 3 is appropriate.

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 starts a job to update embedded data on an existing survey response. It uses specific verbs ('update embedded data') and specifies the resource ('survey response'), distinguishing it from sibling tools like create_response and response import.

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?

Explicitly states that this tool does not support rewriting answer values and directs users to use create_response or response import for that purpose. This provides clear when-to-use and when-not-to-use guidance.

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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