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surveymonkey-mcp

surveymonkey-mcp

by dscovr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

surveymonkey_get_response

Retrieve metadata for a specific survey response by providing survey ID and response ID. Get response details without answers; use the details tool when answer content is needed.

Instructions

Returns metadata for a specific response (no answers). Use surveymonkey_get_response_details for answers.

Args: survey_id: Survey ID. response_id: Response ID.

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": {
      "survey_id": "<survey_id>",
      "response_id": "<response_id>"
    },
    "name": "surveymonkey_get_response"
  }
}

Input Schema

NameRequiredDescriptionDefault
survey_idYes
response_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly sets the expectation that this tool returns metadata only and not answers, which is a meaningful non-obvious behavior. It does not discuss auth, rate limits, or errors, but for a simple read-style getter this is acceptable.

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 extremely concise and front-loaded. The first sentence states the core behavior, the second sentence covers the alternative, and the Args list is minimal and scannable. Every sentence earns its place.

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?

For a simple two-parameter fetch operation with an output schema present, the description covers the key decision point: metadata vs answers. It does not need to explain return values since an output schema exists, and the sibling distinction is fully handled. Slightly more detail on read-only behavior would be nice but is not critical.

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

Parameters2/5

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

The Args section merely restates 'Survey ID' and 'Response ID', which adds no meaningful information beyond what the schema titles already provide. Since schema description coverage is 0%, the description should compensate with additional context, but it does not.

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 'Returns metadata for a specific response' and explicitly notes '(no answers)', which immediately distinguishes it from the sibling surveymonkey_get_response_details. The verb, resource, and scope are all specific.

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 directs users to use surveymonkey_get_response_details when answers are needed, providing a clear when-not/alternative condition. This leaves no ambiguity about which sibling to choose based on the need for answer data.

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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