SurveysAI Qualitative Analysis – interview and focus group analysis

Cancel an analysis

qualitative_cancel_analysis

DestructiveIdempotent

Stops an analysis package for good. Refund rule: if the work failed on our side (refund_on_cancel true in qualitative_get_run), the whole package price is refunded to the credit balance; cancelling a healthy or still running package refunds nothing and the charge stays; a completed package cannot be cancelled. Ask the user before cancelling. Requires an API key with the runs:write scope, and the key's owner must own the analysis. It does not delete the analysis or its data. To resume failed work without cancelling use qualitative_retry_analysis.

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": {
      "run_id": "<run_id>"
    },
    "name": "qualitative_cancel_analysis"
  }
}

Input Schema

Name	Required	Description	Default
`run_id`	Yes	The analysis id (`run_id`) from qualitative_list_runs or qualitative_start_analysis.
`idempotency_key`	No	Optional key (printable ASCII, at most 255 characters). Pass idempotency_key to make your own retries safe: repeating the call with the same key returns the original result instead of acting again. If omitted, every call gets its own fresh key.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already flag destructiveHint=true and idempotentHint=true, but the description goes well beyond them with monetary consequences: full refund only when the failure was on the provider side, no refund for healthy or running packages, and the charge staying on the account. It also states the auth requirement (runs:write scope, key owner must own the analysis) and that data is not deleted.

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 destructive outcome is front-loaded in one short sentence, followed by the refund branch logic and then the auth and non-deletion caveats. Every clause carries decision-relevant information; nothing is redundant padding.

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 two-parameter destructive mutation with no output schema, the description covers what an agent needs: preconditions (ownership, scope, user consent), irreversible cost consequences, what is and is not destroyed, and the safer alternative path.

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 run_id and idempotency_key semantics are already fully documented in the schema, including the retry-safety behavior of the key. The description adds no syntax, format, or edge-case detail about either parameter, so the baseline of 3 is appropriate.

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 opening sentence gives a specific verb and resource ("Stops an analysis package") and clarifies permanence with "for good." It distinguishes itself from the nearest sibling by naming qualitative_retry_analysis as the resume-without-cancel path, and from qualitative_get_run by pointing there for refund eligibility.

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?

Explicit when-to-use and when-not: a completed package cannot be cancelled, and failed work should go through qualitative_retry_analysis instead of cancelling. It also adds a required human-in-the-loop step, "Ask the user before cancelling," which is a real precondition rather than vague advice.

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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