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Delete Survey Response

delete_survey_response
DestructiveIdempotent

Permanently deletes a response by PersonalizedID. Irreversible — for erroneous or test responses. Returns confirmation; deleting a non-existent response reports not-found, not an error, and an unknown survey code is reported as an unknown code — never as an absent response. Verified by readback: the response is re-read afterwards and the reply reports whether it is really gone.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesSurvey publication code — the 8-character code from the survey's public link (e.g. 1a2b3c4d). Returned by list_surveys.
personalizedIDYesThe respondent's PersonalizedID: the value supplied when the respondent/response was created, or the system-generated identifier if none was supplied.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNoWhat the call did, verified, refused or previewed — the complete human-readable outcome.
previewNotrue when the call was a preview (confirm not passed): nothing was changed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already cover destructiveHint/idempotentHint/readOnlyHint, but the description adds real behavioral detail beyond them: not-found is reported as not-found rather than an error, an unknown survey code is distinguished from an absent response, and a readback re-read confirms the deletion actually took effect.

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 irreversible warning and the intended use case are front-loaded, then error and verification semantics follow in a logical order. The readback sentence is slightly verbose but still carries information the caller needs, so nothing is wasteful.

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

Completeness5/5

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

For a destructive mutation with an output schema, the key omission would be error/edge-case behavior, and the description covers exactly that (not-found vs. unknown code vs. real deletion, plus readback confirmation). Nothing an agent needs to decide or call correctly is missing.

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 both parameters (code, personalizedID) are already fully documented in the schema, including formats and provenance. The description only restates that deletion is keyed by PersonalizedID, adding no syntax or edge-case detail beyond the schema.

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?

It states a specific verb and resource ("Permanently deletes a response") and pins the lookup key to PersonalizedID, so the operation is unambiguous. It does not name or contrast with siblings like update_survey_response or get_survey_response, so it falls short of full sibling differentiation.

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?

"Irreversible — for erroneous or test responses" gives a clear intended use case and a strong implicit exclusion (don't use it for legitimate data). It stops short of naming alternatives (e.g., update_survey_response for corrections), so there is no explicit when-not routing.

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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