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tom275275

Google Workspace MCP Server

by tom275275

list_form_responses

Retrieve a list of responses submitted to a Google Form. Provide the user's email and form ID to view answer details with pagination support.

Instructions

List a form's responses.

Args: user_google_email (str): The user's Google email address. Required. form_id (str): The ID of the form. page_size (int): Maximum number of responses to return. Defaults to 10. page_token (Optional[str]): Token for retrieving next page of results.

Returns: str: List of responses with basic details and pagination info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
form_idYes
page_sizeNo
page_tokenNo
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.14.3

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. 'List' implies read-only behavior, and the Returns section indicates a string containing response details and pagination info. However, it does not explicitly state that no mutations occur, what authorization is required, or how pagination tokens are produced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear one-line summary followed by a structured Args/Returns section. It is appropriately sized for a four-parameter tool, with no filler, though the Returns section partially duplicates information that an output schema would already provide.

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 covers the parameters and return payload adequately, but it lacks guidance on when to use this list operation versus get_form_response, and it does not mention authentication prerequisites or failure conditions. Given the low schema coverage and absent annotations, this is a meaningful gap, though the tool itself is straightforward.

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?

Schema description coverage is 0%, so the description must fully compensate, and it does. The Args section explains every parameter: user_google_email is required and identifies the Google user, form_id identifies the form, page_size controls maximum results with a default of 10, and page_token is described as the token for the next page. This makes all four parameters actionable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and the resource ('a form's responses'), so an agent can tell this is a read operation on form responses. It is clear, but it does not explicitly distinguish itself from the similarly named sibling get_form_response.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives such as get_form_response or get_form. It also does not mention prerequisites like authentication via start_google_auth, leaving the agent to infer usage context from the parameter names.

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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