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Remove a store alert

remove_store_alert
DestructiveIdempotent

Removes the signed-in user's alert for a store and alert type ('cashback' or 'gift_card'). Use it for 'stop alerting me about Nike cashback'. Use get_my_alerts to find the store_id and type of an existing alert. Requires OAuth sign-in with a GotCashback account and the 'alerts' scope; anonymous callers are prompted to authorize.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYesGotCashback's numeric store_id, taken from an earlier result (a store, cashback rate, gift card, deals, favorites or alerts response). If you only have a store name, use the by-name tool instead.
alert_typeYesAlert type: 'cashback' (fires on the store's best cashback rate) or 'gift_card' (fires on the store's best gift card discount)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate destructiveHint=true, readOnlyHint=false, and idempotentHint=true. The description adds meaningful context beyond annotations: it requires OAuth sign-in with a specific scope, and mentions that anonymous callers are prompted to authorize. It doesn't contradict annotations and gives the agent useful expectations about side effects and failure modes.

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?

Three sentences, each earning its place: the first states the operation, the second gives a concrete usage example, and the third covers auth requirements and the sibling tool. Information is front-loaded and no filler exists.

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 tool with fully covered schema and annotations, the description covers the essential behavioral and contextual needs: what it removes, how to find inputs, auth requirements, and the natural-language trigger. The lack of an output schema is acceptable since the operation is defined by its side effect.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining that store_id comes from an earlier result and that alert_type has specific firing semantics ('fires on the store's best cashback rate'). This is helpful clarification 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 states a specific verb ('Removes'), a precise resource ('signed-in user's alert for a store and alert type'), and cites an exact user utterance ('stop alerting me about Nike cashback'). It clearly distinguishes this tool from siblings like set_store_alert and get_my_alerts.

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?

It provides explicit when-to-use guidance via a natural-language example, tells the agent to use get_my_alerts to find the required identifiers, and names the alternative tool ('by-name tool') for cases where only a store name is known. This is strong, actionable 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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a clearly distinct lookup or action: store names versus store ids, cashback versus gift cards, brand versus category versus store, and portals versus stores versus user account data. The descriptions also include explicit cross-references telling an agent which tool to prefer, so misselection is unlikely.

Naming Consistency5/5

Tool names follow a consistent get_/set_/remove_/toggle_ verb pattern with resource and qualifier suffixes like by_store_name, by_store_id, and by_country. Singular and plural resource names are used naturally and do not break the overall predictable convention.

Tool Count4/5

18 tools is slightly above the typical well-scoped range, mainly because of parallel by_name and by_id variants for stores, gift cards, and portals. However, each variant serves a distinct workflow and the overall count is coherent for a cashback-comparison and account-management server.

Completeness5/5

The surface covers the full range of the domain: cashback rate lookups, gift card comparisons, brand/category deals, store and portal browsing, payout term checks, and user favorites/alerts with create, update, and delete operations. There are no obvious dead ends, and the cross-references between tools make workflows like finding a store_id and then setting an alert seamless.

Resources