GotCashback

Cashback rates by store name

get_cashback_rates_by_store_name

Read-onlyIdempotent

Finds a store by name and returns its current cashback rates from every cashback portal that lists it — online and in-store, percentage or fixed amount, with 'up to' flags — so the user can compare every portal and see the best. This is the preferred first call for any cashback question that names a store: 'best cashback for Walmart', 'highest Nike cashback', 'cashback at Expedia', 'Walmart cashback today', 'compare Walmart cashback portals', 'Best Buy in-store cashback', 'Dell cashback in Germany'. No prior lookup is needed — do not call get_stores_by_name, get_stores_by_country or get_countries first. Returns every matching store (best match first, one entry per country), each with its 'cashback_rates'; an empty list means no match — retry with a shorter name or without country_code. Use get_cashback_rates_by_store_id only when a store_id is already known, get_gift_cards_by_store_name for gift card discounts, and get_best_deals_by_brand when the user asks where to buy a brand's products rather than about a specific store. Rates reflect GotCashback's current data, refreshed several times a day. Present every returned portal's rate to the user (a table with a link column, not only the best one) and always show each rate's 'url' as a clickable link — cashback is only credited when the user clicks through it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`store_name`	Yes	The retailer or brand name as a shopper would say it, e.g. 'Walmart', 'Nike', 'Best Buy', 'Expedia', 'Home Depot'. Matched case-insensitively at word boundaries of the store name and its known alternate names (exact name, then names starting with it, then names containing it as a word), best match first; a partial-word match (e.g. 'gap' inside 'Singapore') is only returned when nothing better exists. Pass the plain name only — no words like 'cashback', 'gift card' or a country.
`country_code`	No	Optional. Lowercase ISO 3166-1 alpha-2 country code that limits results to one country, e.g. 'us' (United States), 'ca' (Canada), 'gb' (United Kingdom — use 'gb', not 'uk'), 'de' (Germany), 'au' (Australia), 'fr' (France). Pass it when the user names a country ('in Canada', 'UK cashback'); omit it to search all supported countries.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`stores`	No	Matching stores (one entry per country). Empty when nothing matched.

Schema Changelog

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

Changed1 schema field changedSep 7, 2026

changedInput schema / properties / store_name / description

Previous value: -"The retailer or brand name as a shopper would say it, e.g. 'Walmart', 'Nike', 'Best Buy', 'Expedia', 'Home Depot'. Matched as a case-insensitive substring of the store name and its known alternate names, best match first. Pass the plain name only — no words like 'cashback', 'gift card' or a country."New value: +"The retailer or brand name as a shopper would say it, e.g. 'Walmart', 'Nike', 'Best Buy', 'Expedia', 'Home Depot'. Matched case-insensitively at word boundaries of the store name and its known alternate names (exact name, then names starting with it, then names containing it as a word), best match first; a partial-word match (e.g. 'gap' inside 'Singapore') is only returned when nothing better exists. Pass the plain name only — no words like 'cashback', 'gift card' or a country."

First observedSep 6, 2026

TDQS

A5/5.0

Behavior5/5

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

The description aligns with the annotations (readOnly, idempotent, non-destructive) and adds useful behavioral context. It explains data freshness ('refreshed several times a day') and instructs how to present results to ensure users click through links for cashback credit. No contradictions with the annotation metadata.

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 description is comprehensive yet well-organized. It front-loads the core purpose and then proceeds logically through examples, exclusions, usage guidance, and presentation rules. Each sentence adds valuable information without unnecessary repetition. The structure makes it easy for an agent to extract all necessary instructions.

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?

Given the two parameters and 100% schema coverage, the description provides complete context. It explains the output format (matching stores with cashback rates), how to handle empty results, and the presentation requirement. It also covers edge cases like partial matches and country-specific usage. All necessary operational details are included.

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

Parameters5/5

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

Both parameters are fully described with detailed semantics beyond the schema. The store_name description covers matching logic, case-insensitivity, word boundaries, and partial-word fallback, with examples. The country_code description explains the format, valid examples, and when to pass it vs. omit it. This goes well beyond the basic schema definitions.

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 clearly states the tool's purpose: finding a store by name and returning cashback rates from all portals. It explicitly differentiates from sibling tools like get_cashback_rates_by_store_id and get_best_deals_by_brand, and provides concrete example queries. The primary use case (any cashback question that names a store) is unambiguous.

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 gives explicit when-to-use and when-not-to-use guidance. It states 'No prior lookup is needed' and names the tools to avoid calling first. It also specifies alternative tools for different scenarios (store_id, gift cards, brand product queries) and provides presentation requirements (show all rates, clickable links). This is thorough and actionable.

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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