Этот проект реализует сервер Model Context Protocol (MCP) для управления транзакциями эфирного времени с использованием Africa's Talking API . Он предоставляет набор инструментов для проверки баланса счета, отправки эфирного времени, просмотра последних транзакций пополнения, суммирования сумм последних пополнений и подсчета пополнений для определенного номера телефона. Приложение использует SQLite для хранения данных транзакций и поддерживает африканские страны, поддерживаемые Africa's Talking Airtime Service, с правильным форматированием телефонных номеров.
Сервер MCP Africa's Talking Airtime интегрируется с API Africa's Talking Airtime для упрощения передачи эфирного времени. Основные функции включают:
Отправка эфирного времени на указанные номера телефонов.
Хранение сведений о транзакциях в базе данных SQLite.
Извлечение и обобщение истории транзакций.
Проверка баланса счета на Africa's Talking.
Приложение поддерживает страны, в которых поддерживается услуга Africa Talking Airtime.
Related MCP server: mpesa-mcp
Чтобы автоматически установить Africa's Talking Airtime Server для Claude Desktop через Smithery :
npx -y @smithery/cli install @nasoma/africastalking-airtime-mcp --client claudePython 3.10 или выше
curl -LsSf https://astral.sh/uv/install.sh | shЧтобы настроить и запустить проект локально, выполните следующие действия:
Клонируйте репозиторий:
git clone https://github.com/nasoma/africastalking-airtime-mcp.git
cd africastalking-airtime-mcpНастройте виртуальную среду и установите зависимости, выполнив:
uv sync Вы готовы идти!
Добавьте это в ваш claude_desktop_config.json :
{
"mcpServers": {
"Airtime Server": {
"command": "{{PATH_TO_UV}}", // Run `which uv` and place the output here
"args": [
"--directory",
"{{PATH_TO_PROJECT}}", // cd into the repo, run `pwd` and enter the output here
"run",
"main.py"
],
"env": {
"username": "your_africastalking_username",
"api_key": "your_africastalking_api_key",
"country":"your_country", # e.g kenya, uganda, dr congo, rwanda, south africa
"currency_code":"currency-code" # e.g. KES, UGX, NGN
}
}
}
}Goose — хороший вариант, если вы хотите использовать предпочитаемую вами LLM и предоставить ключ API.
Установите Goose.
Откройте панель настроек и добавьте пользовательское расширение (MCP Server).
Дайте вашему расширению имя. Тип — STDIO.
Добавьте команду. Сохраните изменения.
Добавьте переменные среды: username , api_key , currency_code и country .
Сохраните изменения.
MCP предоставляет следующие инструменты для управления транзакциями эфирного времени:
проверка_баланса :
Описание : Возвращает текущий баланс эфирного времени для вашего аккаунта Africa's Talking.
Использование : check_balance()
Вывод : Возвращает остаток на счете (например, «Остаток на счете: KES 1234.00») или сообщение об ошибке, если остаток не может быть получен.
load_airtime :
Описание : Отправляет эфирное время на указанный номер телефона и сохраняет транзакцию в базе данных.
Параметры :
phone_number : номер телефона получателя (например, «0712345678» или «+254712345678»).
amount : количество отправленного эфирного времени (например, 100).
currency_code : Код валюты (например, «KES»).
Использование : load_airtime("0712345678", 100.00, "KES")
Вывод : подтверждение успеха (например, «Успешно отправлено 100,00 KES на номер +254712345678») или сообщение об ошибке.
get_last_topups :
Описание : Извлекает последние N транзакций пополнения счета из базы данных.
Параметры :
limit : количество транзакций для извлечения (по умолчанию: 3).
Использование : get_last_topups(3)
Вывод : список последних транзакций (например, «Последние 3 транзакции пополнения: ...») или указание на отсутствие транзакций.
сумма_последних_n_пополнений :
Описание : Рассчитывает общую сумму последних N успешных пополнений, гарантируя, что они используют одну и ту же валюту.
Параметры :
n : Количество транзакций для суммирования (по умолчанию: 3).
Использование : sum_last_n_topups(3)
Вывод : Возвращает сумму (например, «Сумма последних 3 успешных пополнений: KES 300.00») или ошибку, если валюты различаются.
count_topups_by_number :
Описание : Подсчитывает количество успешных пополнений счета определенного номера телефона.
Параметры :
phone_number : номер телефона для запроса (например, «0712345678»).
Использование : count_topups_by_number("0712345678")
Вывод : Возвращает количество (например, «Количество успешных пополнений счета +254712345678: 5») или ошибку.
Ниже приведены примеры вопросов или команд, которые пользователи могут задать ИИ для взаимодействия с MCP африканского Talking Airtime, на основе доступных инструментов:
Каков баланс моего счета Africa's Talking?
Можете ли вы показать мне текущий баланс?
Проверьте мой баланс эфирного времени.
Отправьте 100 KES эфирного времени на номер 0712345678.
Пополните мой счет 0712345678 на 60.
Пополните счет на 50 NGN на номер +2348012345678.
Можете ли вы пополнить счет на 200 UGX на номер 0755123456?
Покажите мне последние 3 транзакции эфирного времени.
Каковы мои последние пополнения?
Перечислите последние 5 пополнений счета.
Какова общая сумма моих последних трех пополнений?
Сложите суммы моих последних 4 транзакций по эфирному времени.
Какую сумму я отправил за последние 5 пополнений?
Сколько раз я пополнял счет 0712345678?
Учитывайте пополнения на счете +254712345678.
Скажите, сколько успешных пополнений было совершено на номер 0755123456?
Убедитесь, что на вашем счете Africa's Talking достаточно средств для отправки эфирного времени.
Номера телефонов автоматически форматируются на основе переменной country , установленной в клиенте или в claude_desktop_config.json .
База данных SQLite ( airtime_transactions.db ) создается в каталоге проекта при инициализации.
Лучше всего работает с моделями, которые поддерживают вызов инструментов, например, Claude 3.7 Sonnet . Если вы заботитесь о цене GPT-4.1 Nano — хороший и более дешевый вариант при использовании с такими клиентами, как Goose.
API говорящей Африки Документация говорящей Африки .
Checks the airtime balance of the Africa's Talking account.
This tool connects to the Africa's Talking API to fetch the user's
current application data, which includes the account balance.
Returns:
str: A message displaying the account balance or an error if the
balance cannot be retrieved.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. It effectively describes the tool's behavior: connecting to an external API, fetching application data, and returning either a balance message or an error. It covers the core operation and error handling, though it lacks details like rate limits, authentication requirements, or specific error conditions.
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 appropriately sized and front-loaded, starting with the core purpose. The second sentence adds necessary detail about the API connection, and the 'Returns' section clarifies output behavior. It's efficient with minimal waste, though the 'Returns' formatting could be slightly more concise.
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 the tool's low complexity (0 parameters, no output schema, no annotations), the description is reasonably complete. It explains what the tool does, how it operates via API, and what it returns. For a simple balance check tool, this covers essential context, though it could benefit from more detail on error scenarios or API specifics.
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 input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description adds value by confirming no parameters are needed ('checks the airtime balance' implies a simple query without inputs), aligning with the schema. Baseline is 4 for 0 parameters, as it provides clear semantic context.
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 specific action ('Checks') and resource ('airtime balance of the Africa's Talking account'), distinguishing it from sibling tools that focus on top-up operations rather than balance inquiry. It precisely defines what the tool does without ambiguity.
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 context by mentioning it fetches 'application data' and 'account balance', suggesting it's for checking current balance status. However, it doesn't explicitly state when to use this tool versus alternatives like checking transaction history through sibling tools, nor does it provide exclusion criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Counts the number of top-ups for a specific phone number.
Args:
phone_number (str): The phone number to count transactions for.
Returns:
str: The total count of top-ups for the given number or an error message.
| Name | Required | Description | Default |
|---|---|---|---|
| phone_number | Yes |
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. It states the tool counts top-ups and returns a count or error message, but lacks details on permissions, rate limits, error conditions, or data freshness. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 well-structured and front-loaded with the core purpose, followed by clear sections for arguments and returns. Every sentence earns its place by directly supporting tool understanding, with no redundant or verbose language, making it efficient and easy to parse.
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 the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameter semantics but lacks behavioral details like error handling or usage guidelines. For a simple query tool, it meets minimum viability but has clear gaps in contextual richness.
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 meaningful context for the single parameter 'phone_number' by explaining it is 'the phone number to count transactions for,' which clarifies its role beyond the schema's basic type and title. With schema description coverage at 0%, the description effectively compensates by providing semantic value, though it could include format examples (e.g., E.164).
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's purpose with a specific verb ('Counts') and resource ('number of top-ups for a specific phone number'), distinguishing it from siblings like 'check_balance' (balance checking), 'get_last_topups' (retrieving recent transactions), 'load_airtime' (airtime loading), and 'sum_last_n_topups' (summing recent transactions). It precisely defines what the tool does without ambiguity.
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 provides no guidance on when to use this tool versus alternatives. It does not mention scenarios where counting top-ups is preferred over other tools like 'get_last_topups' or 'sum_last_n_topups', nor does it specify prerequisites or exclusions. Usage is implied only by the purpose statement, lacking explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves the last N top-up transactions from the database.
Args:
limit (int, optional): The number of recent transactions to fetch.
Defaults to 3.
Returns:
str: A formatted string listing the last N transactions or a message
if no transactions are found.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
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. It describes the tool as retrieving transactions and returning a formatted string or message, which covers basic behavior. However, it lacks details on error handling, data format specifics, or performance aspects like rate limits or permissions needed, leaving gaps in transparency.
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 appropriately sized and front-loaded, starting with a clear purpose statement followed by structured sections for arguments and returns. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, parameter semantics, and return behavior. However, it could improve by addressing potential edge cases or linking to sibling tools for better context, slightly reducing completeness.
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 meaningful context for the single parameter 'limit,' explaining it as 'the number of recent transactions to fetch' with a default of 3, which goes beyond the input schema's basic type and title. Since schema description coverage is 0%, the description compensates well by providing clear semantics for the parameter.
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's purpose with specific verb ('retrieves') and resource ('last N top-up transactions from the database'). It distinguishes from siblings by focusing on recent transactions rather than checking balances, counting by number, loading airtime, or summing amounts.
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 context by specifying 'last N top-up transactions,' suggesting it's for recent transaction review rather than other operations like checking balances or loading airtime. However, it does not explicitly state when to use this tool versus alternatives like 'sum_last_n_topups' or 'count_topups_by_number,' missing explicit exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sends airtime to a specified phone number and logs the transaction.
This tool formats the phone number, sends the airtime using the
Africa's Talking API, and saves a record of the transaction in the
database.
Args:
phone_number (str): The recipient's phone number.
amount (float): The amount of airtime to send.
currency_code (str): The currency for the transaction (e.g., "KES").
Returns:
str: A message indicating the status of the airtime transaction.
| Name | Required | Description | Default |
|---|---|---|---|
| phone_number | Yes | ||
| amount | Yes | ||
| currency_code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses key behaviors: formatting phone numbers, using Africa's Talking API, and logging to a database. However, it lacks details on error handling, rate limits, authentication needs, or what specific data is logged.
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 well-structured and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds necessary detail without redundancy. The Args/Returns sections are clearly formatted and efficiently convey parameter and return value information.
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 mutation tool with no annotations and no output schema, the description does a good job covering purpose, parameters, and basic behavior. However, it lacks details about the return message format, error conditions, or database logging specifics that would be helpful for an agent.
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 0%, so the description must compensate. It provides clear semantic meaning for all three parameters: phone_number identifies the recipient, amount specifies airtime quantity, and currency_code defines transaction currency with an example ('KES'). This adds significant value beyond the bare schema.
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's purpose with specific verbs ('sends airtime', 'logs the transaction') and identifies the resource (phone number). It distinguishes from sibling tools like check_balance or get_last_topups by focusing on sending rather than querying.
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 sending airtime via Africa's Talking API, but provides no explicit guidance on when to use this tool versus alternatives like count_topups_by_number or sum_last_n_topups. No prerequisites, exclusions, or comparison to siblings are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculates the sum of the last 'n' successful top-ups.
This tool retrieves the last 'n' transactions from the database and
calculates their total sum. It ensures that all transactions are in the
same currency before summing.
Args:
n (int, optional): The number of recent top-ups to sum. Defaults to 3.
Returns:
str: The total sum of the last 'n' top-ups or an error message if
the currencies are mixed or no transactions are found.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses key behavioral traits: it only sums successful top-ups, retrieves from a database, ensures currency consistency, and handles error cases (mixed currencies or no transactions). It doesn't cover aspects like performance, rate limits, or authentication needs, but provides substantial operational context beyond basic functionality.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by operational details, then clearly formatted Args and Returns sections. Every sentence adds value—none are redundant or vague—and the structure enhances readability without unnecessary elaboration.
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 the tool's moderate complexity (database query with calculations and error handling), no annotations, and no output schema, the description is largely complete: it covers purpose, parameters, behavior, and return values. It could benefit from mentioning prerequisites (e.g., authentication) or performance characteristics, but adequately addresses core functionality and error cases for an agent to use it correctly.
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 meaningful semantics beyond the input schema: it explains that 'n' represents 'the number of recent top-ups to sum' with a default of 3, and clarifies it's optional. With 0% schema description coverage (schema only has type and title), the description fully compensates by providing clear parameter meaning and usage context.
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's purpose with specific verbs ('calculates', 'retrieves', 'ensures') and resources ('last n successful top-ups', 'transactions from the database'). It distinguishes from siblings like 'check_balance' (current balance), 'count_topups_by_number' (counts by number), 'get_last_topups' (lists without summing), and 'load_airtime' (different operation).
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 summing recent top-ups, but doesn't explicitly state when to use this tool versus alternatives like 'get_last_topups' (for listing) or 'count_topups_by_number' (for counting by number). It mentions currency consistency as a requirement, which provides some contextual guidance, but lacks explicit when/when-not scenarios or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_balancecount_topups_by_numberget_last_topupsload_airtimesum_last_n_topupsScored across 5 tools
Each tool has a clearly distinct purpose with no overlap: check_balance for account status, load_airtime for sending airtime, and three tools (count_topups_by_number, get_last_topups, sum_last_n_topups) for analyzing transaction history from different angles. The descriptions reinforce these distinct roles, making misselection unlikely.
The naming follows a consistent snake_case pattern with clear verb_noun structures (e.g., check_balance, load_airtime). However, there is a minor deviation: 'count_topups_by_number' uses a prepositional phrase instead of a simple noun, slightly breaking the pattern compared to tools like 'get_last_topups' or 'sum_last_n_topups'.
With 5 tools, the count is well-scoped for an airtime management server. It covers core operations (checking balance, sending airtime) and transaction analysis without being overwhelming, ensuring each tool earns its place in the workflow.
The toolset provides good coverage for airtime operations: checking balance, sending airtime, and analyzing transaction history. A minor gap exists in not having tools for managing transaction errors or refunds, but agents can likely work around this with the existing tools for core workflows.
Prepaid SMS OTP and transactional SMS API. Eight priced markets: NG, GH, KE, ZA, GB, BJ, CI, US.
Send and schedule SMS and WhatsApp messages, manage contacts and templates, and track delivery.
Send, automate, and orchestrate SMS, WhatsApp, Viber, IVR, and USSD messaging in 150+ countries.
Mobile money fees, African tax rates, currency conversion and Uganda PAYE for AI agents.
