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ThriveCart: Cancel subscription

thrivecart_cancel_subscription

ThriveCart: Stop all future payments of a subscription. Cannot be undone. Cancels one active or paused subscription for good. ThriveCart tells the payment processor, tags the original transaction as cancelled and adds a $0.00 transaction on that day to mark it. It does not refund anything. By default you confirm each call; you can switch that off for this tool. Reaches other people: confirm with the user before each call. Needs the user's Cotlet API key with access to this app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subscription_idYesSubscription ID from list_subscriptions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior1/5

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

The description is behaviorally rich (processor notification, tagging the original transaction, a $0.00 marker transaction, no refund, per-call confirmation), but it directly conflicts with the annotations: it states the cancellation is permanent and 'Cannot be undone ... for good', while destructiveHint=false declares the operation non-destructive. An agent or downstream policy gate trusting the hint would treat an irreversible cancellation as safe.

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 nature and back-end side effects are front-loaded, which is the right priority. It loses a point for redundancy: two separate sentences ('By default you confirm each call; you can switch that off' and 'confirm with the user before each call') cover essentially the same confirmation requirement.

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 one-parameter mutation with no output schema, the description covers irreversibility, the absence of refunds, downstream processor effects, confirmation behavior, and the required API key scope. The only real gap is the unresolved conflict with the destructiveHint annotation.

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% and the single parameter's description already points to list_subscriptions as the source of the ID. The description adds no parameter-level syntax or format detail, so the schema does all the work and the baseline 3 applies.

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?

States a specific verb and resource ('Stop all future payments of a subscription') plus scope ('cancels one active or paused subscription for good'), which clearly separates it from the sibling thrivecart_pause_subscription and thrivecart_refund_transaction. An agent knows exactly what state change this performs without opening the schema.

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?

Gives real decision context: the action is permanent ('Cannot be undone'), it 'does not refund anything', and each call should be confirmed with the user. It does not, however, explicitly name the alternative (thrivecart_pause_subscription) for stopping payments temporarily, leaving that routing to inference.

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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