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awardmath-mcp

by DanCastHub
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Python
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AwardMath MCP server

awardmath-mcp подключает AI-ассистентов к публичному API AwardMath через Model Context Protocol.

AwardMath — это оптимизатор туристических баллов. Вы указываете свои балансы баллов и цель поездки. Детерминированный решатель перебирает версионированный граф коэффициентов перевода и таблиц вознаграждений, а затем возвращает до трёх проверенных и объяснимых планов бронирования.

Расчёт — это перебор и скоринг по опубликованным таблицам. LLM может разобрать ваш вопрос и объяснить результат, но никогда не выбирает и не оптимизирует планы.

Этот репозиторий содержит MCP-сервер и бесплатный срез данных в каталоге data/. Сервер работает только на чтение. Для него не нужны API-ключ, логин или подписка, и он не собирает данные с сайтов авиакомпаний.

Инструменты

Сервер предоставляет три инструмента.

Инструмент

Возможности

list_programs

Перечисляет отслеживаемые и расходуемые программы

solve_award_trip

Возвращает до трёх планов

parse_wallet

Структурирует текст кошелька

list_programs

list_programs возвращает каждую программу, которую отслеживает AwardMath, и определяет, какие идентификаторы программ решатель может задействовать сегодня.

К расходуемым программам относятся программы возна с ценами из таблиц вознаграждений, а также банковские валюты, которые переводятся в одну из таких программ. Это различие не позволяет ассистенту попросить решатель потратить баланс, у которого нет пути к оплачиваемому вознаграждению.

solve_award_trip

solve_award_trip ищет планы, подходящие под ваш кошелёк, пункты отправления, назначения, класс обслуживания, количество путешественников и лимит наличных расхов.

Успешный ответ включает до трёх планов со следующими данными:

  • точные шаги перевода

  • общее количество требуемых баллов

  • указанные сборы

Если ни один план не подходит, в ответе содержится no_plan_reasons. Там указана причина, по которой ограничение не было выполнено, а не заполняется пробел приблизительным маршрутом или ценой.

parse_wallet

parse_wallet превращает произвольный текст в структурированный баланс.

Например:

250k Amex, 80k Chase

Понятные балансы становятся идентификаторами программ и суммами. Неднозначные фразы выводятся на уточнение, а не определяются на глаз.

Related MCP server: mcp-travelcode

Установка и запуск

Вы можете запустить сервер прямо из GitHub или установить его из клона.

Claude Code

claude mcp add awardmath -- uvx --from git+https://github.com/DanCastHub/awardmath-mcp awardmath-mcp

Claude Desktop или другой MCP-клиент с JSON-конфигурацией

{
  "mcpServers": {
    "awardmath": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/DanCastHub/awardmath-mcp", "awardmath-mcp"]
    }
  }
}

Из копии репозитория

pip install -e .
awardmath-mcp

Не нужен никакой API-ключ или учётная запись AwardMath.

Пример

Попросите своего ассистента составить план с ценой на основе вашего текущего остатка баллов.

Вы: У меня 60 000 баллов Amex Membership Rewards. Найдите одно место в эконом-классе по маршруту LAX—HNL.

Ассистент: По состоянию на 2026-08-23, лучший по цене план использовал 16 000 Qatar Avios для этого места, полученные через перевод Amex в соответствии 1:1. Ответ включает точные шаги перевода, общее количество баллов и комиссии. Он не подтверждает, что рейс доступен.

Этот результат актуален только на дату. Курсы перевода и цены вознаграждений меняются. Запустите новое решение, проконсультируйтесь с вознаграждениями и проверьте условия перевода перед переброской баллов.

Данные

Каталог data/ содержит датированный срез графа наград и выходных данных решателя.

data/transfer-ratios-current.csv

Этот файл содержит 109 ребер перевода «банк—программа» для восьми исходных валют, по состоянию, зафиксированному на 2026-08-23.

Каждая строка является самым последним наблюдением, которое AwardMath проверил по странице программы или подтвердил через независимые источники. Редакционные заявления, не проверенные таким образом, были исключены.

Включенные столбцы:

  • коэффициент перевода

  • комиссия

  • время перевода, где подтверждено

  • статус проверки

  • дата наблюдения

Курсы перевода меняются. Считайте, что каждая строка действительна только на дату наблюдения, и перед переводом баллов проверяйте программу.

data/sample-solved-plans.json

Этот файл содержит пять сценариев, рассчитанных на работающем решателе 2026-08-23.

Четыре сценария дали планы, в том числе:

  • LAX—HNL в эконом-классе с 60,000 баллов Amex Membership Rewards

  • SFO—HND в бизнес-классе с 100,000 баллов Amex Membership Rewards, через ANA за 82 500 баллов

Одним из сценариев намеренно отсутствует. Так, сценарий бизнес-класса по маршруту YVR—DXB показывает, как решатель реагирует, когда граф не может назначить цену маршрута. Он сообщает о недостатке охвата графа, а не придумывает план.

Чего здесь нет

Набор данных — это срез, а не полный продуктых AwardMath. Версионированная история сохраняется на awardmath.com. В этой истории со временем отражаются курсы перевода, таблицы вознаграждений и вызовы истории цен или решений «бронируй сейчас—подожди». Сюда она не включена.

Что этот сервер не делает

Сервер не проверяет наличие премиальных мест в буке в реальном времени.

Планы собраны по опубликованным таблицам вознаграждений и условиям перевода. Маршрут может быть математически обоснован, но на нужные вам даты мест может не быть.

Прежде чем переводить какие-либо баллы:

  1. Проверьте наличие премиальных мест через Seats.aero, PointsYeah или авиакомпанию.

  2. Уточните цену в баллах и сборы в программе бронирования.

  3. Перепроверьте коэффициент и переводовые условия.

Перевод баллов необратим. Не переводите их только потому, что решатель нашёл цену. Переводите, когда подтвердите, что место можно забронировать.

AwardMath проверяет математику. Вы подтверждаете место.

Лицензии

Код распространяется по лицензии MIT.

Файлы внеш участии data/ распространяются под CC BY 4.0. Укажите это атрибуция со ссылкой:

Данные: AwardMath

Источники:

Available Tools

3 tools
list_programsA

List every rewards program AwardMath tracks.

Returns {"names": {program_id: display name}, "plannable": [program_id]}. "plannable" ids are the ones a solve can spend today: award programs with priced charts, plus bank currencies that transfer into one. Use these ids in solve_award_trip wallets.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.4/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It precisely outlines the return structure (an object with 'names' and 'plannable' fields) and elaborates on the meaning of 'plannable' (award programs with priced charts plus bank currencies that transfer into one). This gives the agent solid insight into what the tool returns and on what basis, well beyond a generic 'list' statement.

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 three concise sentences: the first states the purpose, the second defines the return shape, and the third explains the 'plannable' field and its intended use. Every sentence contributes meaningful information with no redundancy or padding, making it efficiently structured and easy to parse.

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 that the tool has no parameters and no output schema, the description is exceptionally complete. It fully defines the return object, explains the semantics of each field, and explicitly ties the output to the sibling tool solve_award_trip. There are no critical gaps that would prevent an agent from correctly invoking the tool and interpreting its results.

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 tool has zero parameters and the input schema is empty, so the description has no parameter semantics to add. Per the rubric, a tool with zero parameters gets a baseline of 4. The description appropriately focuses on output and usage rather than parameters, so the score reflects this baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with 'List every rewards program AwardMath tracks,' which is a clear verb and resource definition. It does not explicitly differentiate from sibling tools like parse_wallet or solve_award_trip, but the listing nature is unmistakable given the name and the different purposes of those siblings. Thus it is specific but lacks explicit sibling distinction.

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 goes beyond just stating what it does by explaining when to use the output: 'Use these ids in solve_award_trip wallets.' This directly connects the tool to a sibling and prescribes how the returned 'plannable' ids should be consumed. It also clarifies which programs qualify as 'plannable,' giving the agent a clear decision rule for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

parse_walletA

Turn free text about points balances into structured wallet entries.

Example input: "I have 250k Amex MR, 80k Chase, and my wife has 60k Alaska". Returns {"balances": [{owner, program, balance, expires}], "unrecognized": [...], "needs_clarification": [...]}. Anything ambiguous lands in the latter two lists instead of being guessed — show it to the user rather than dropping it. If you already know the balances in structured form, skip this and build the wallet directly.

ParametersJSON Schema
NameRequiredDescriptionDefault
textYes

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it delivers: it spells out the return shape (balances, unrecognized, needs_clarification), and crucially states the no-guessing policy — 'Anything ambiguous lands in the latter two lists instead of being guessed — show it to the user rather than dropping it.' This is a meaningful behavioral trait. It stops short of edge-case handling (e.g., empty text), keeping it from a 5.

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 front-loaded with the purpose, followed by a compact example, the return schema, and the behavioral contract. Each sentence earns its place. It runs slightly long due to the dual example-and-schema-format block, but nothing is redundant.

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 single-parameter parse tool with no output schema, the description is nearly complete: it states the input, enumerates the three output lists, and defines the ambiguity contract. The absence of an output schema means the description must explain return values, which it does. Minor omission: it doesn't state a reasonable fallback for degenerate input, but the tool's complexity doesn't demand it.

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 0% for the single required parameter, so the description must compensate. The example input ('I have 250k Amex MR, 80k Chase...') conveys the expected free-text format with program names and amounts, which is the main semantic need. However, the description never explicitly defines the parameter's contract beyond the example — it leaves the agent to infer that text is a raw user message about point balances.

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 opening line 'Turn free text about points balances into structured wallet entries' uses a specific verb (turn/parse), a defined resource (free text about points balances), and a concrete outcome (structured wallet entries). It clearly differentiates from siblings list_programs and solve_award_trip, which serve materially different purposes.

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 description provides an explicit when-not-to-use condition: 'If you already know the balances in structured form, skip this and build the wallet directly.' This tells an agent precisely when the tool is unnecessary. It implies when to use it (when balances exist only as free text) via the example, though it doesn't formally exclude siblings — acceptable since the siblings are not genuine alternatives to a text-parsing operation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

solve_award_tripA

Compute up to three verified award-booking plans for a trip.

wallet: [{"owner": "me", "program": "amex-mr", "balance": 250000}] — ids from list_programs; "expires" (ISO date) optional per entry. origins / destinations: IATA airport codes, e.g. ["LAX"] -> ["HNL"]. cabins: any of "economy", "premium", "business", "first" (default economy). max_cash_usd: cap on out-of-pocket cash (fees, points purchases). window_from / window_to: ISO dates, optional.

Returns plans with exact transfer steps, points math, and fees, plus no_plan_reasons when nothing works — an empty result explains itself. Plans price the award from published charts; they never assert live seat availability, so tell the user to confirm space before transferring anything irreversible.

ParametersJSON Schema
NameRequiredDescriptionDefault
cabinsNo
walletYes
originsYes
travelersNo
window_toNo
window_fromNo
destinationsYes
max_cash_usdNo

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full responsibility. It explicitly states plans are priced from published charts and never assert live seat availability, and that empty results include no_plan_reasons. This is a strong disclosure of key behavioral limitations and return expectations, though it doesn't cover every potential edge case.

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 purpose is front-loaded, followed by a systematic per-parameter breakdown and behavioral caveats. The text is longer than average but every sentence adds value; there is no fluff or redundant wording.

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 tool with 8 parameters, no annotations, and no output schema, the description covers purpose, inputs, high-level output (plans with transfer steps, points math, fees, no_plan_reasons), and a critical limitation (no live availability). It does not describe the exact output structure, but the summary is sufficient for an agent to call and interpret results.

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 coverage is 0%, so the description must compensate. It explains wallet with an example, origins/destinations with IATA codes, cabins with options and default, max_cash_usd as a cap, and window dates as ISO optional. It covers 7 of 8 parameters but omits 'travelers', leaving its meaning to inference—a minor but notable gap.

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 opens with a specific verb-resource pair ('Compute up to three verified award-booking plans for a trip') and the context clearly distinguishes it from siblings list_programs and parse_wallet. The 'verified' and 'up to three' qualifiers add specificity without ambiguity.

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 description provides clear context for when to use the tool: after obtaining wallet IDs from list_programs, and it instructs the agent to confirm live availability before irreversible transfers. It does not explicitly name alternatives or exclusions, but the sibling relationships are implicit, so this is adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedlist_programs
    • First observedparse_wallet
    • First observedsolve_award_trip
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: list_programs enumerates available programs, parse_wallet converts free text into structured wallet entries, and solve_award_trip computes booking plans. There is zero overlap between them, so an agent can easily select the right tool for each step of the workflow.

Naming Consistency5/5

All three tool names follow a consistent verb_noun pattern: list_programs, parse_wallet, solve_award_trip. The verbs are clear and the nouns match the domain, making the naming predictable and intuitive.

Tool Count5/5

Three tools is well-scoped for the server's purpose: listing programs, parsing input, and solving trips. Each tool is necessary and sufficient for the end-to-end workflow, with no redundancy or missing helper clutter.

Completeness4/5

The tools cover the full lifecycle: discovering what programs exist, structuring user input, and computing booking plans with transfer steps. A minor gap is the lack of a dedicated tool for fetching detailed program rules or live availability, but the solve tool's output includes sufficient details and reasons for failures, so most practical needs are met.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

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