bank-mcp
🏦 bank-mcp
Предоставьте своему ИИ-ассистенту безопасный доступ в режиме «только чтение» к вашим банковским счетам.
Большинство людей управляют своими финансами, заходя на банковские порталы, скачивая CSV-файлы и создавая электронные таблицы. bank-mcp устраняет эти сложности, позволяя вашему ИИ-ассистенту напрямую запрашивать данные о ваших банковских счетах — балансы, транзакции, детализацию расходов — с помощью естественного общения. Он подключается к реальным банковским API через Model Context Protocol, поэтому любой MCP-совместимый клиент (Claude Code, Claude Desktop и другие) может понимать ваши финансы.
5 провайдеров, 15 000+ учреждений — охват банков США и Европы
Только чтение по дизайну — нет доступа на запись, нет переводов, нет изменений
Работает с любым MCP-клиентом — Claude Code, Claude Desktop, Cursor и другими
Плагинная архитектура — добавьте своего провайдера менее чем за 100 строк кода
Содержание
Related MCP server: Lunch Flow MCP Server
Поддерживаемые провайдеры
Провайдер | Регион | Учреждения | Метод аутентификации | Сложность настройки |
Европа | 2 000+ | RSA-ключ + сессия | Средняя | |
США | 7 000+ | mTLS-сертификат | Средняя | |
США / Канада / ЕС | 12 000+ | Client ID + secret | Легкая | |
Европа | 3 400+ | OAuth2-токен | Легкая | |
Mock | Демо | — | Нет | Мгновенно |
Банки США
Поддерживаются через Plaid и Teller — охватывают 20 крупнейших банков США и тысячи других:
JPMorgan Chase · Bank of America · Wells Fargo · Citibank · Capital One · U.S. Bank · PNC · Truist · Goldman Sachs · TD Bank · Citizens · Fifth Third · M&T Bank · Huntington · KeyBank · Ally · Regions · BMO · American Express · USAA
Европейские банки
Поддерживаются через Enable Banking и Tink — охватывают основные банки ЕС и Великобритании:
HSBC · BNP Paribas · Deutsche Bank · ING · Crédit Agricole · Santander · Société Générale · UniCredit · Intesa Sanpaolo · Barclays · Lloyds · BBVA · CaixaBank · Commerzbank · Rabobank · ABN AMRO · Swedbank · Handelsbanken · Nordea · PKO Bank Polski
Быстрый старт
1. Запустите мастер настройки
npx @bank-mcp/server initИнтерактивный мастер проведет вас через все этапы — выбор провайдера, учетные данные, авторизация в банке и проверка счета — и все это с удобным терминальным интерфейсом:
┌ bank-mcp — Connect your bank account
│
◇ Choose your banking provider
│ Plaid / Teller / Tink / Enable Banking
│
◇ Environment
│ Sandbox / Development / Production
│
◇ Found 3 account(s) ─────────────────────────╮
│ ****1591 (Bank of America Platinum Card) │
│ ****3588 (Bank of America My Checking) │
│ ****2450 (Bank of America Essential Savings)│
├───────────────────────────────────────────────╯
│
└ Setup complete!2. Добавьте в свой MCP-клиент
В конце настройки мастер спросит, какой MCP-клиент вы используете, и покажет точную конфигурацию:
Claude Code — одна команда:
claude mcp add bank -- npx @bank-mcp/serverCursor — добавьте в
.cursor/mcp.jsonWindsurf — добавьте в
~/.codeium/windsurf/mcp_config.jsonGemini CLI — добавьте в
~/.gemini/settings.jsonCodex CLI — добавьте в
~/.codex/config.json
Используете другой инструмент? См. Настройка клиента для всех поддерживаемых клиентов, включая Claude Desktop, VS Code и Zed.
3. Попробуйте
Спросите своего ИИ-ассистента о ваших финансах на естественном языке:
"What's my checking account balance?"
"Show my spending by category this month"
"Find all Amazon purchases over $50"
"Compare my spending this month vs last month"Демо-режим
У вас еще нет банковских учетных данных? Начните с реалистичных фиктивных данных:
npx @bank-mcp/server --mockЭто запустит сервер с фиктивным провайдером, который генерирует детерминированные примеры счетов и транзакций — идеально подходит для тестирования настройки или разработки на базе bank-mcp перед подключением реальных счетов.
Настройка клиента
bank-mcp работает с любым MCP-совместимым клиентом. Выберите свой инструмент ниже.
Claude Code
Добавьте в .mcp.json в корне вашего проекта (или ~/.claude/.mcp.json для всех проектов):
{
"mcpServers": {
"bank": {
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}Или добавьте через CLI:
claude mcp add bank -- npx @bank-mcp/serverClaude Desktop
Добавьте в ваш claude_desktop_config.json:
{
"mcpServers": {
"bank": {
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}Расположение файла конфигурации:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Cursor
Добавьте в .cursor/mcp.json в корне вашего проекта (или ~/.cursor/mcp.json глобально):
{
"mcpServers": {
"bank": {
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}VS Code (Copilot)
Добавьте в .vscode/mcp.json в вашем рабочем пространстве:
{
"servers": {
"bank": {
"type": "stdio",
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}Windsurf
Добавьте в ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"bank": {
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}OpenAI Codex CLI
Добавьте в ~/.codex/config.toml (или .codex/config.toml в вашем проекте):
[mcp_servers.bank]
command = "npx"
args = ["@bank-mcp/server"]Или добавьте через CLI:
codex mcp add bank -- npx @bank-mcp/serverGemini CLI
Добавьте в ~/.gemini/settings.json (или .gemini/settings.json в вашем проекте):
{
"mcpServers": {
"bank": {
"command": "npx",
"args": ["@bank-mcp/server"]
}
}
}Zed
Добавьте в ваш Zed settings.json:
{
"context_servers": {
"bank": {
"command": {
"path": "npx",
"args": ["@bank-mcp/server"]
}
}
}
}Не видите свой инструмент? bank-mcp использует стандартный транспорт MCP stdio. Любой клиент, поддерживающий MCP stdio-серверы, может подключиться, используя
npx @bank-mcp/serverв качестве команды.
Доступные инструменты
Инструмент | Описание | Ключевые параметры |
| Список всех банковских счетов по подключениям |
|
| Получение транзакций с фильтрацией |
|
| Полнотекстовый поиск по описаниям и продавцам |
|
| Текущие и доступные балансы |
|
| Расходы, сгруппированные по продавцу или категории |
|
Скриншоты
Все примеры ниже используют Claude Code с фиктивным провайдером (npx @bank-mcp/server --mock).
Список счетов — «Покажи мои банковские счета»

Проверка баланса — «Какой у меня текущий баланс?»

История транзакций — «Покажи мои транзакции за последние 15 дней»

Поиск транзакций — «Найди все покупки в Starbucks за последние 2 недели»

Расходы по категориям — «Покажи мои расходы по категориям в этом месяце»

Топ продавцов — «На каких продавцов я трачу больше всего?»

Отслеживание подписок — «Покажи мои регулярные подписки»

Сравнение продуктов — «Сравни расходы в Trader Joe's и Whole Foods»

Полная финансовая картина — «Дай мне полную финансовую картину за февраль»

Архитектура
Структура файлов
~/.bank-mcp/
config.json # Connections & credentials (permissions: 600)
keys/ # RSA keys and certificates
src/
providers/
base.ts # Abstract BankProvider class
registry.ts # Provider registration
enable-banking/ # PSD2 via Enable Banking API
teller/ # US banks via mTLS
plaid/ # US/CA/EU via Plaid API
tink/ # EU Open Banking via Tink API
mock/ # Deterministic fake data
tools/ # MCP tool implementations
utils/
cache.ts # In-memory TTL cache
http.ts # Fetch with timeout + retryИнтерфейс провайдера
Каждый провайдер расширяет один и тот же абстрактный класс, что упрощает добавление новых интеграций:
abstract class BankProvider {
abstract listAccounts(config): Promise<BankAccount[]>;
abstract listTransactions(config, accountId, filter?): Promise<Transaction[]>;
abstract getBalance(config, accountId): Promise<Balance[]>;
abstract getConfigSchema(): ConfigField[];
}Руководства по настройке провайдеров
Enable Banking (PSD2)
Что вам нужно:
[ ] Учетная запись Enable Banking с зарегистрированным приложением
[ ] Ваш закрытый RSA-ключ (файл
.pem, загруженный при создании приложения)
npx @bank-mcp/server init
# Select: Enable Banking → enter App ID + key path
# Pick your country → select your bank
# Log in at your bank → paste the redirect URL
# → Session created, accounts verified!Совет: Мастер берет на себя весь процесс OAuth — настройку URI перенаправления, выбор банка и создание сессии. Сессии истекают через 90 дней (регулирование PSD2); перезапустите
initдля обновления.
Teller (Банки США)
Что вам нужно:
[ ] Учетная запись разработчика Teller
[ ] Ваш идентификатор приложения (из панели управления Teller)
npx @bank-mcp/server init
# Select: Teller → enter Application ID
# Pick environment (sandbox for testing)
# → Teller Connect opens in your browser
# → Link your bank, token captured automatically!Совет: Начните с песочницы (sandbox) — сертификаты не нужны, мгновенные тестовые данные. Для разработки/продакшена мастер запросит пути к mTLS-сертификатам. Бесплатный уровень поддерживает до 100 активных подключений.
Plaid (США/Канада/ЕС)
Что вам нужно:
[ ] Учетная запись разработчика Plaid (бесплатная регистрация)
[ ] Ваш Client ID и Secret (из панели управления Plaid)
npx @bank-mcp/server init
# Select: Plaid → enter client ID + secret
# Pick environment (sandbox for testing)
# → Sandbox: token created automatically!
# → Dev/Prod: paste an existing access tokenСовет: Начните с песочницы (sandbox) — мастер автоматически создает тестовый токен, браузер не нужен. Plaid предоставляет самую богатую категоризацию транзакций — 104 подкатегории с оценками достоверности — идеально подходит для анализа расходов с помощью LLM.
Tink (Открытый банкинг ЕС)
Что вам нужно:
[ ] Учетная запись разработчика Tink (бесплатно для тестирования)
[ ] Ваш Client ID и Client Secret (из консоли Tink)
npx @bank-mcp/server init
# Select: Tink → enter Client ID + Secret
# Pick your market (country)
# → Tink Link opens in your browser
# → Connect your bank, paste redirect URLСовет: Tink охватывает более 3400 банков по всей Европе. Для песочницы используйте Demo Bank с тестовыми учетными данными (показаны в мастере). Транзакции включают категории PFM с обогащением данных о продавцах.
Кэширование
Все данные кэшируются в оперативной памяти (без сохранения на диск — кэш удаляется вместе с процессом):
Данные | TTL | Почему |
Список счетов | 1 час | Счета редко меняются; минимизирует вызовы API |
Транзакции | 15 минут | Баланс между новыми транзакциями и свежестью данных |
Балансы | 5 минут | Самые чувствительные ко времени; пользователи ожидают актуальные данные |
Кэш привязан к подключению и счету. Перезапуск сервера очищает все кэши.
Несколько подключений
Настройте столько банковских подключений, сколько вам нужно — даже от разных провайдеров:
{
"connections": [
{ "id": "ing-main", "provider": "enable-banking", "..." : "..." },
{ "id": "chase-checking", "provider": "plaid", "..." : "..." },
{ "id": "revolut", "provider": "tink", "..." : "..." }
]
}Все инструменты принимают необязательный параметр connectionId для выбора конкретного подключения. Если он опущен, опрашиваются все подключения, а результаты объединяются — поэтому «покажи все мои балансы» автоматически работает для всех банков.
Безопасность
Принципы проектирования
bank-mcp обрабатывает чувствительные финансовые учетные данные. Его подход к безопасности основан на минимизации поверхности атаки:
Только чтение по дизайну — интерфейс
BankProviderпредоставляет только методы чтения (listAccounts,listTransactions,getBalance). Нет методов записи — нет переводов, нет изменений счетов, нет инициирования платежей. Это обеспечивается на уровне типов, а не по соглашению.Нет сетевого слушателя — bank-mcp работает как процесс stdio (stdin/stdout), а не как HTTP-сервер.
Available Tools
5 toolsget_balanceA
Get current account balance(s). Returns closing booked balance and expected balance when available.
| Name | Required | Description | Default |
|---|---|---|---|
| connectionId | No | ||
| accountId | No | Account UID. If omitted, returns balances for all accounts. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It does mention the return types ('closing booked balance' and 'expected balance'), which is helpful, but it does not address whether the tool is read-only, if it requires authentication (implicit via connectionId), or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, no redundant words, and directly addresses the tool's purpose and output. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple balance retrieval tool with two parameters and no output schema, the description is mostly complete. It covers the output type and the optionality of accountId. However, it could clarify terms like 'closing booked balance' and 'expected balance' for better clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only accountId has a description). The description for accountId adds useful context: 'If omitted, returns balances for all accounts.' However, connectionId lacks a description in both schema and tool description, leaving its meaning unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'current account balance(s)', and specifies that it returns 'closing booked balance and expected balance'. This distinguishes it from sibling tools like list_accounts or list_transactions, which deal with other account data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when to prefer get_balance over list_accounts or spending_summary, or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_accountsB
List all bank accounts across configured connections. Returns account UIDs, IBANs, names, and currencies.
| Name | Required | Description | Default |
|---|---|---|---|
| connectionId | No | Connection ID to query. If omitted, queries all connections. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must cover behavioral traits. It does not mention that this is a read-only operation, nor any potential performance considerations, rate limits, or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences: one describing the action and scope, one describing the output. No redundant information, efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema and no annotations, the description is adequate but lacks usage guidelines and behavioral context. It covers the basic purpose and return fields but not when or how to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the single parameter with a clear description. The tool description adds no additional semantics beyond stating it lists accounts, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all bank accounts across configured connections and specifies the returned fields (UIDs, IBANs, names, currencies). It distinguishes from siblings like get_balance or list_transactions by focusing on account listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., get_balance for a single account). No explicit conditions or prerequisites provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_transactionsB
List bank transactions with optional filters. Defaults to last 90 days. Supports date range, amount range, and debit/credit type filtering.
| Name | Required | Description | Default |
|---|---|---|---|
| connectionId | No | Connection ID. If omitted, queries all connections. | |
| accountId | No | Account UID. If omitted, queries all accounts. | |
| dateFrom | No | Start date (YYYY-MM-DD). Defaults to 90 days ago. | |
| dateTo | No | End date (YYYY-MM-DD). Defaults to today. | |
| amountMin | No | Minimum absolute amount. | |
| amountMax | No | Maximum absolute amount. | |
| type | No | Filter by transaction type. | |
| limit | No | Maximum number of transactions to return. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses default date range and optional filters, but does not state that the operation is read-only, nor mention pagination, rate limits, or any side effects. This is a significant gap for a tool with 8 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action and resource. Every sentence adds value: first states purpose and filters, second gives default behavior. No waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with 8 parameters and no output schema, the description covers defaults and filter types, but does not explain return value, pagination behavior, or typical usage scenarios. Schema descriptions fill some gaps, but overall completeness is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds little beyond the schema – it mentions 'debit/credit type filtering' which is already in the enum, and 'amount range' which is covered by 'amountMin' and 'amountMax'. No new semantic insight.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists bank transactions with optional filters. It is a specific verb-resource pairing. However, it does not differentiate from sibling 'search_transactions', which may have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing transactions with filters and mentions a default 90-day window, but lacks explicit guidance on when to use this tool versus alternatives like 'search_transactions' or 'spending_summary'. No exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_transactionsA
Full-text search across transaction descriptions, merchant names, and references. Use for finding specific payments or payees.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search text — matched against description, merchant name, and reference. | |
| connectionId | No | ||
| dateFrom | No | ||
| dateTo | No | ||
| limit | No | Max results. Default 50. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It describes the search operation but does not disclose whether it is read-only, any performance implications, pagination behavior, or error handling. The word 'search' implies read but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long with no extraneous words. The first sentence states the action, the second provides usage context. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters (1 required) and no output schema. While the purpose is clear, the description fails to explain optional parameters like connectionId, dateFrom, dateTo, and does not describe return format or behavior for edge cases. This leaves gaps for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 40% (only query and limit have descriptions). The description adds semantics for query (full-text across specific fields) but does not explain connectionId, dateFrom, or dateTo. With low schema coverage, the description should compensate but does not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'full-text search across transaction descriptions, merchant names, and references' with a specific use case of 'finding specific payments or payees'. This distinctly separates it from sibling tools like list_transactions which likely list all transactions without search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the tool ('for finding specific payments or payees') but does not explicitly mention when not to use it or compare to alternatives like list_transactions. The guidance is clear but lacks explicit exclusionary context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
spending_summaryC
Group expenses by merchant or category with totals. Shows where money is being spent. Use groupBy "merchant" for vendor breakdown, "category" for category breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| connectionId | No | ||
| dateFrom | No | ||
| dateTo | No | ||
| groupBy | No | Group expenses by "merchant" (default) or "category". | |
| limit | No | Max groups to return (default 20, sorted by total spent). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It mentions grouping and totals but omits critical behavioral details such as the ability to filter by date range (dateFrom, dateTo) and the default limit and sorting behavior, which are only present in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief, with three clear sentences that front-load the purpose. It avoids unnecessary detail and is easy to parse, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given five parameters and no output schema, the description omits important context such as the meaning of dateFrom/dateTo for filtering and the default limit of 20. It also lacks any hint of the return format beyond 'totals', making it incomplete for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal value beyond the input schema: it reiterates the groupBy options but does not explain the purpose of connectionId, dateFrom, dateTo, or limit beyond what the schema already provides. With 40% schema coverage, the description should compensate more.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool groups expenses by merchant or category with totals, showing where money is spent. It distinguishes from sibling tools like list_transactions and get_balance by focusing on aggregation rather than raw data or balances.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description suggests when to use each groupBy option but does not provide explicit guidance on when to use this tool versus alternatives like search_transactions or list_transactions. The context is implied but not directly contrasted.
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.
5 tool updates
v0.1.0- First observed
get_balance - First observed
list_accounts - First observed
list_transactions - First observed
search_transactions - First observed
spending_summary
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: get_balance for balances, list_accounts for account listing, list_transactions for filtered transaction lists, search_transactions for full-text search, spending_summary for aggregation. No overlap.
All tool names use a consistent snake_case verb_noun or descriptive pattern (get_balance, list_accounts, list_transactions, search_transactions, spending_summary). No mixing of conventions.
5 tools is well-scoped for a banking data retrieval server. Each tool covers a core function without redundancy, and the count feels natural for the domain.
The set covers balance, accounts, transactions (with search and filters), and spending summaries. Minor gaps include no individual transaction detail endpoint, but search can retrieve specifics. Overall solid coverage for read-only banking information.
Maintenance
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Connect AI agents to bank accounts, transactions, balances, and investments.
Chat with your bank data: balances, transactions, budgets, bills. Reads only, never moves money.
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