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Survey Solutions MCP Server

Survey Solutions MCP Server

by lbruce-wq
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

prepare_designer_questionnaire_import

Prepare human-in-the-loop instructions for manually importing a Designer questionnaire into Headquarters when no public import API exists.

Instructions

Prepare human-in-the-loop instructions for importing a questionnaire from Designer into Headquarters when no public import API is available.

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": {
      "workspace_name": "<workspace_name>"
    },
    "name": "prepare_designer_questionnaire_import"
  }
}

Input Schema

NameRequiredDescriptionDefault
profileNo
import_commentNo
workspace_nameYes
questionnaire_nameNo
after_import_next_stepsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.7

TDQS

A3.7/5.0
Behavior4/5

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

There are no annotations, so the description carries the behavioral burden. It transparently indicates that the tool does not perform an import itself but prepares instructions for a human to execute, and it discloses the relevant context of missing public API availability. It could add more detail about the expected output format, but the core non-mutating behavior is clear.

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?

The description is a single, tightly written sentence that leads with the action and states the essential condition. There is no redundant or filler content.

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

Completeness2/5

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

The description is adequate for tool selection but incomplete for correct invocation: it does not describe what the generated instructions look like, how they are returned, or how the five parameters should be populated. With no output schema and no annotation support, this is a meaningful gap.

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

Parameters1/5

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

Schema description coverage is 0% and the description provides no meaning for any of the five parameters. Parameter names like workspace_name, profile, import_comment, questionnaire_name, and after_import_next_steps are left entirely unexplained, so an agent cannot determine what values to supply.

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 uses a specific verb ('Prepare') and a specific resource ('human-in-the-loop instructions for importing a questionnaire from Designer into Headquarters'). It also states the key condition ('when no public import API is available'), which distinguishes it from any potential direct-import sibling.

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 clearly states when the tool is appropriate: when no public import API is available. It provides a clear context for use, though it does not explicitly name alternatives or exclusion cases.

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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