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dscovr

surveymonkey-mcp

by dscovr

surveymonkey_create_survey

Create a new SurveyMonkey survey by supplying a JSON definition with title, pages, and questions.

Instructions

Creates a new SurveyMonkey survey.

Args: survey_definition: Survey definition as a JSON object. Structure: { "title": "My Survey", (required) "nickname": "Internal name", (optional) "language": "en", (optional, default: "en") "folder_id": "123", (optional) "category": "general", (optional) "pages": [ (optional — add pages at creation) { "title": "Page 1", "questions": [ { "headings": [{"heading": "Question text"}], "family": "single_choice", "subtype": "vertical", "answers": { "choices": [ {"text": "Option A"}, {"text": "Option B"} ] } } ] } ] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
survey_definitionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations, the description carries the burden of explaining the tool's side effects. It states that a new survey is created and notes that pages may be added at creation time, but it does not mention auth requirements, error behavior, or what happens after successful creation. This is adequate but not rich.

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 front-loaded with a one-sentence purpose, then a well-organized Args section. The JSON structure is detailed but every line is relevant to constructing a valid survey_definition; there is no filler or redundancy.

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 the complex nested survey definition and sparse schema, the description provides enough structure to create a basic survey with pages and questions. It does not cover every possible SurveyMonkey field or validation constraint, but the output schema exists and the provided examples cover the common creation flow well.

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

Parameters5/5

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

The input schema is nearly empty (only 'survey_definition' as an object with additionalProperties: true), and schema description coverage is 0%. The description fully compensates by providing a detailed JSON structure with required fields, optional fields, and nested pages/questions, adding substantial meaning beyond the schema.

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 opens with 'Creates a new SurveyMonkey survey,' which names a specific verb and resource. This clearly differentiates the tool from siblings like surveymonkey_update_survey, surveymonkey_patch_survey, and surveymonkey_delete_survey.

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 frames the tool as a create operation, making it obvious it should be used for new surveys rather than updating or deleting existing ones. It does not explicitly name alternative tools, but the create-versus-update context is strong enough for an agent to route correctly.

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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