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Price Drop Back: Can I get the difference back?

pricedropback_check_price_drop_claim
Read-onlyIdempotent

Price Drop Back. Can I get the difference back?. Use for "I bought a TV at Best Buy 9 days ago for $499 and it's $449 now, can I get the difference?". Says whether a claim is likely (likely, likely_if_member, unlikely, check or no_drop), the amount, and the steps. With an item name it adds a Cheapest Price link and Amazon and eBay links to check today's price. Likely claims also get claim_message, ready to paste into the store's chat or email. policy.refund_paid_as flags stores that pay store credit (Newegg)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNoToday's price, 0 to 100000 (US dollars, or pounds for UK stores).
itemNoOptional item name, up to 100 characters, to link today's price on Cheapest Price, Amazon and eBay.
paidNoWhat you paid, 0 to 100000 (US dollars, or pounds for UK stores).
storeYesStore name, up to 60 characters.
countryNoTwo-letter country code. GB (or UK) gives the UK policy where the store has one (Apple, Amazon, IKEA) and UK rules; it also picks the price links (US, GB and IE get local Amazon and eBay links). Default from the request.
purchase_dateNoThe date you bought it, YYYY-MM-DD, instead of days_since_purchase. Adds deadlines: the last day to claim with the store (and in a members' longer window) and Capital One card protection.
days_since_purchaseNoDays since you bought it (or since delivery), 0 to 400. Give this or purchase_date.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare the read-only, idempotent, non-destructive, closed-world profile. The description adds valuable output context beyond that: the likelihood enums, the amount, steps, claim_message, and the policy.refund_paid_as flag for store-credit stores. No output schema exists, so this return-value disclosure is genuinely useful.

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?

Front-loaded with purpose and example, followed by output details. The repeated title fragment ('Price Drop Back. Can I get the difference back?.') is slightly redundant, but overall the sentences are information-dense and earn their place.

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 7-parameter check tool with no output schema, the description covers what the tool returns (likelihood, amount, steps, links, claim_message) and the refund_paid_as caveat. It is largely complete, though it could clarify country/store edge behavior a bit more.

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 parameters like store, paid, now, country, and dates are already fully documented. The description only adds marginal meaning (item name triggers price links). Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('check price drop claim') and frames it as a consumer question ('Can I get the difference back?'), with a concrete example. An agent can distinguish it from siblings like get_store_policy or get_claim_reminder from the description alone.

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?

The 'Use for ...' example is an effective, concrete usage trigger that shows exactly what kind of request maps to this tool. It lacks explicit when-not guidance or pointers to sibling tools (e.g., get_store_policy for raw policy text), so it stops short of 5.

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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