GotCashback

Add or remove a favorite store

toggle_favorite_store

Idempotent

Adds a store to, or removes it from, the signed-in user's favorite stores. Use it for 'add Best Buy to my favorites' or 'remove Nike from my favorites'. Needs the store_id: take it from an earlier result, or find it with get_stores_by_name (optionally with country_code). Requires OAuth sign-in with a GotCashback account; anonymous callers are prompted to authorize. All comparison tools work without signing in. Scope: 'favorites'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`favorite`	Yes	true to add the store to favorites, false to remove it
`store_id`	Yes	GotCashback'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.

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the annotations, the description discloses that this mutates user-specific state, requires OAuth sign-in, prompts anonymous callers to authorize, and scopes the operation to 'favorites'. This meaningfully supplements the idempotentHint and destructiveHint annotations without contradicting them.

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 moderately long but every sentence carries useful information: purpose, examples, parameter sourcing, auth requirements, and scope. It is front-loaded with the core semantics and avoids fluff, though a couple of phrases could be tightened.

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 simple two-parameter tool with no output schema, the description covers purpose, parameter provenance, authentication, scope, and alternatives. An agent has everything needed to select and invoke the tool correctly in common scenarios.

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?

The input schema already documents both parameters with 100% coverage, so the baseline is 3. The description adds value by explaining where store_id can come from, suggesting get_stores_by_name as a lookup path, and mentioning the optional country_code for that lookup.

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 precise operation—adding or removing a store from the signed-in user's favorite stores—with concrete example utterances for both directions. It clearly differentiates this mutation tool from read-only siblings like get_my_favorite_stores and from store-lookup tools.

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?

The description explicitly tells the agent when to use the tool via natural language examples, and it routes store_id lookup to get_stores_by_name when only a store name is available. It also notes that comparison tools work without signing in, giving helpful context about when this tool is not needed.

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 distinct resource or query dimension: store name vs. store ID, brand vs. category vs. store, portal list vs. portal search, and account actions. Descriptions include explicit routing rules for ambiguous cases like a brand that is also a store name.

Naming Consistency5/5

All tools use lowercase snake_case with a consistent verb-first pattern: get_ for reads, set_/remove_/toggle_ for mutations. The singular/plural variation in get_portal_by_id vs. get_portals_by_name is logical and follows result cardinality.

Tool Count4/5

18 tools is slightly above the typical well-scoped 3-15 range, but each tool covers a distinct access path or lifecycle action. The by-name/by-id variants could be consolidated, but the breadth of the cashback, gift card, portal, and account domains justifies the count.

Completeness5/5

The tool surface covers the full read workflow for countries, stores, rates, gift cards, portals, and deals, plus account lifecycle for favorites and alerts. Store IDs from lookup tools feed directly into ID-based lookups and account mutations, so there are no obvious dead ends.

Resources

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