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tool_estimate_points_earning

Calculate points earned on a purchase for a specific credit card and spending category. View base earn rate, multiplier, and estimated USD value to choose the best card.

Instructions

Calculate points earned on a purchase for a specific credit card and spending category.

Read-only. No auth required. Uses static bonus multiplier data (standard published rates; does not reflect temporary promotions). Returns: points_earned, base_earn_rate, applied_multiplier, program name, and estimated_value_usd at mid-range cpp. Supports 9 cards: chase_sapphire_reserve, chase_sapphire_preferred, amex_platinum, amex_gold, capital_one_venture_x, citi_premier, bilt_mastercard, ink_preferred, amex_green.

Use this when the user wants to know which card to swipe for a specific purchase. Use tool_calculate_points_or_cash to decide whether to pay cash or redeem points. Use tool_estimate_points_value to evaluate a specific award booking's cpp.

Args: amount: Purchase amount (in the specified currency) card_key: Card identifier — chase_sapphire_reserve, amex_gold, bilt_mastercard, etc. category: Spending category — dining, travel, flights, restaurants, supermarkets, rent, general (default: general) currency: ISO currency code of the purchase (e.g., "USD", "EUR")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountYes
card_keyYes
categoryNogeneral
currencyNoUSD
Behavior5/5

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

Discloses read-only nature, no auth required, uses static bonus multiplier data excluding temporary promotions, and lists return fields. No annotations provided, so description carries full burden and meets it well.

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?

Concise yet comprehensive. Front-loads core purpose, then adds behavioral notes, usage guidelines, and parameter details. No redundant sentences.

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 complexity (4 parameters, no output schema, no annotations), description covers purpose, usage, parameters, return values, limitations, and relationships to siblings. Fully adequate for agent decision-making.

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?

With 0% schema coverage, description adds significant meaning: explains amount purpose, enumerates supported card keys, gives category examples, and specifies currency format. Missing precise format for currency but still highly informative.

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?

Description clearly states the tool calculates points earned for a purchase with a specific credit card and spending category. It distinguishes from sibling tools by name and purpose.

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?

Explicitly advises when to use this tool ('when the user wants to know which card to swipe for a specific purchase') and provides alternatives for related decisions.

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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