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ActivityWatch MCP Server

Name: ActivityWatch MCP Server
Author: 8bitgentleman

by 8bitgentleman

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TypeScript

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MCP-сервер ActivityWatch

Сервер протокола Model Context Protocol (MCP), который подключается к ActivityWatch, позволяя LLM, таким как Claude, взаимодействовать с вашими данными трекинга времени.

Возможности

Список бакетов (List Buckets): Просмотр всех доступных бакетов ActivityWatch
Выполнение запросов (Run Queries): Выполнение мощных запросов на языке AQL (ActivityWatch Query Language)
Получение необработанных событий (Get Raw Events): Получение событий напрямую из любого бакета
Получение настроек (Get Settings): Доступ к конфигурационным настройкам ActivityWatch

Related MCP server: Paprika SQL MCP Server

Установка

Вы можете установить MCP-сервер ActivityWatch через npm или собрав его самостоятельно.

Установка через npm (скоро)

# Global installation
npm install -g activitywatch-mcp-server

# Or install locally
npm install activitywatch-mcp-server

Сборка из исходного кода

Клонируйте этот репозиторий:

git clone https://github.com/8bitgentleman/activitywatch-mcp-server.git
cd activitywatch-mcp-server

Установите зависимости:
```
npm install
```
Соберите проект:
```
npm run build
```

Предварительные требования

Установленный и запущенный ActivityWatch
Node.js (версии 14 или выше)
Claude for Desktop (или любой другой MCP-клиент)

Использование

Использование с Claude for Desktop

Откройте файл конфигурации Claude for Desktop:
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Добавьте конфигурацию MCP-сервера:

{
"mcpServers": {
    "activitywatch": {
    "command": "activitywatch-mcp-server",
    "args": []
    }
}
}

Если вы собрали сервер из исходного кода, используйте:

{
"mcpServers": {
    "activitywatch": {
    "command": "node",
    "args": ["/path/to/activitywatch-mcp-server/dist/index.js"]
    }
}
}

Перезапустите Claude for Desktop
Найдите значок MCP в интерфейсе Claude, чтобы убедиться, что всё работает

Использование rootless-контейнера podman в Linux с Gemini CLI

Сначала соберите образ с помощью команды:

version=$(npm pkg get version | tr -d '"')
podman build . -t activitywatch-mcp-server:${version}

В этом примере используется переопределение для случая, когда Activity Watch недоступен по адресу 127.0.0.1 (см. следующий раздел). Если это не требуется, вы можете опустить переменную окружения AW_API_BASE.

{
  "mcpServers": {
    "activitywatch-mcp-server": {
      "command": "/usr/bin/podman",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--userns=keep-id",
        "-e",
        "AW_API_BASE",
        "localhost/activitywatch-mcp-server:1.2.1"
      ],
      "env": {
        "AW_API_BASE": "http://mydesktop.local:5600/api/0"
      }
    }
  }
}

Переопределение хоста/порта сервера ActivityWatch

Если вы хотите запустить этот MCP-сервер из подсистемы Windows для Linux (WSL), например, внутри контейнера, сервер AW, запущенный в Windows, будет недоступен по адресу 127.0.0.1. Чтобы переопределить стандартное подключение к localhost, используйте переменную окружения AW_API_BASE или флаг --aw-api-base, как показано ниже:

# Using environment variable
export AW_API_BASE=http://mydesktop.local:5600/api/0
node dist/index.js

# Or using command-line flag
node dist/index.js --aw-api-base=http://mydesktop.local:5600/api/0

ПРИМЕЧАНИЕ: Сервер AW может быть требователен к имени, используемому для подключения, но он примет имя, соответствующее имени компьютера, на котором он запущен, с суффиксом .local.

Примеры запросов

Вот несколько примеров запросов, которые вы можете попробовать в Claude:

Список всех ваших бакетов: "Какие у меня есть бакеты ActivityWatch?"
Получение сводки использования приложений: "Можешь показать, какими приложениями я пользовался больше всего сегодня?"
Просмотр истории посещений: "На каких сайтах я провел больше всего времени сегодня?"
Проверка продуктивности: "Сколько времени я сегодня провел в приложениях для продуктивности?"
Просмотр настроек: "Каковы мои настройки ActivityWatch?" или "Можешь проверить конкретную настройку в ActivityWatch?"

Доступные инструменты

list-buckets

Выводит список всех доступных бакетов ActivityWatch с опциональной фильтрацией по типу.

Параметры:

type (опционально): Фильтрация бакетов по типу (например, "window", "web", "afk")
includeData (опционально): Включить данные бакета в ответ

run-query

Выполнение запроса на языке запросов ActivityWatch (AQL).

Параметры:

timeperiods: Период(ы) времени для запроса, отформатированные как массив строк. Для диапазонов дат используйте формат: ["2024-10-28/2024-10-29"]
query: Массив операторов запроса на языке AQL, где каждый элемент — это полный запрос с операторами, разделенными точкой с запятой
name (опционально): Имя запроса (используется для кэширования)

ВАЖНО: Каждая строка запроса должна содержать полный запрос с несколькими операторами, разделенными точкой с запятой.

Пример формата запроса:

{
  "timeperiods": ["2024-10-28/2024-10-29"],
  "query": ["events = query_bucket('aw-watcher-window_UNI-qUxy6XHnLkk'); RETURN = events;"]
}

Обратите внимание:

timeperiods должны содержать предварительно отформатированные диапазоны дат с косой чертой
Каждый элемент в массиве query — это полный запрос со всеми операторами

get-events

Получение необработанных событий из бакета ActivityWatch.

Параметры:

bucketId: ID бакета, из которого нужно получить события
start (опционально): Дата/время начала в формате ISO
end (опционально): Дата/время окончания в формате ISO
limit (опционально): Максимальное количество возвращаемых событий

get-settings

Получение настроек ActivityWatch с сервера.

Параметры:

key (опционально): Получить конкретный ключ настроек вместо всех настроек

Примеры языка запросов

ActivityWatch использует простой язык запросов. Вот несколько распространенных шаблонов:

// Get window events
window_events = query_bucket(find_bucket("aw-watcher-window_"));
RETURN = window_events;

// Get only when not AFK
afk_events = query_bucket(find_bucket("aw-watcher-afk_"));
not_afk = filter_keyvals(afk_events, "status", ["not-afk"]);
window_events = filter_period_intersect(window_events, not_afk);
RETURN = window_events;

// Group by app
window_events = query_bucket(find_bucket("aw-watcher-window_"));
events_by_app = merge_events_by_keys(window_events, ["app"]);
RETURN = sort_by_duration(events_by_app);

// Filter by app name
window_events = query_bucket(find_bucket("aw-watcher-window_"));
code_events = filter_keyvals(window_events, "app", ["Code"]);
RETURN = code_events;

Конфигурация

По умолчанию сервер подключается к API ActivityWatch по адресу http://localhost:5600. Если ваш экземпляр ActivityWatch запущен на другом хосте или порту, вы можете переопределить его, как описано в разделе "Переопределение хоста/порта сервера ActivityWatch" выше.

Устранение неполадок

ActivityWatch не запущен

Если ActivityWatch не запущен, сервер будет выдавать ошибки подключения. Убедитесь, что ActivityWatch запущен и доступен по указанному адресу хоста/порта (http://localhost:5600, если вы его не переопределили).

Ошибки запросов

Если вы столкнулись с ошибками запросов:

Проверьте синтаксис запроса
Убедитесь, что ID бакетов верны
Проверьте, содержат ли периоды времени (timeperiods) данные
Проверьте логи ActivityWatch для получения дополнительной информации

Проблемы с форматированием запросов Claude/MCP

Если Claude сообщает об ошибках при выполнении запросов через этот MCP-сервер, скорее всего, это связано с проблемами форматирования. Убедитесь, что ваш запрос в промптах соответствует этому точному формату:

{
  "timeperiods": ["2024-10-28/2024-10-29"],
  "query": ["events = query_bucket('aw-watcher-window_UNI-qUxy6XHnLkk'); RETURN = events;"]
}

Распространенные проблемы:

Периоды времени отформатированы неправильно (должны быть "start/end" в виде одной строки внутри массива)
Операторы запроса разделены на отдельные элементы массива вместо того, чтобы быть объединенными в одну строку

Самая распространенная проблема форматирования

Самая частая ошибка — когда Claude разбивает каждый оператор запроса на отдельный элемент массива, вот так:

{
  "query": [
    "browser_events = query_bucket('aw-watcher-web');",
    "afk_events = query_bucket('aw-watcher-afk');",
    "RETURN = events;"
  ],
  "timeperiods": ["2024-10-28/2024-10-29"]
}

Это НЕВЕРНО. Вместо этого все операторы должны находиться в одной строке внутри массива:

{
  "timeperiods": ["2024-10-28/2024-10-29"],
  "query": ["browser_events = query_bucket('aw-watcher-web'); afk_events = query_bucket('aw-watcher-afk'); RETURN = events;"]
}

При составлении промптов для Claude

При составлении промптов для Claude будьте очень точны в отношении формата и используйте примеры. Например, скажите:

"Выполни запрос с timeperiods ["2024-10-28/2024-10-29"] и query ["statement1; statement2; RETURN = result;"]. Важно: убедитесь, что ВСЕ операторы запроса находятся в одной строке внутри массива, а не разделены на отдельные элементы массива."

Вклад в проект

Мы приветствуем ваш вклад! Пожалуйста, не стесняйтесь присылать Pull Request.

Лицензия

MIT

Available Tools

5 tools

activitywatch_get_eventsC

Get raw events from an ActivityWatch bucket

ParametersJSON Schema

Name	Required	Description
`bucketId`	Yes	ID of the bucket to fetch events from
`limit`	No	Maximum number of events to return (default: 100)
`start`	No	Start date/time in ISO format (e.g. '2024-02-01T00:00:00Z')
`end`	No	End date/time in ISO format (e.g. '2024-02-28T23:59:59Z')

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't mention whether this is a read-only operation, if it requires authentication, potential rate limits, error conditions, or what format the 'raw events' are returned in. The description is functionally adequate but lacks important operational context.

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 extremely concise - a single sentence that communicates the essential purpose without any wasted words. It's front-loaded with the core functionality and doesn't include unnecessary elaboration, making it efficient for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'raw events' means in terms of data structure, doesn't mention any limitations or constraints, and provides no context about the ActivityWatch system. The agent would need to guess about the return format and operational characteristics.

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?

With 100% schema description coverage, the input schema already documents all 4 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value regarding parameter usage or semantics.

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 clearly states the action ('Get raw events') and resource ('from an ActivityWatch bucket'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'activitywatch_query_examples' or 'activitywatch_run_query', which might also retrieve event data but with different approaches or formats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. With sibling tools like 'activitywatch_query_examples' and 'activitywatch_run_query' that might serve similar purposes, there's no indication of when this raw event retrieval is preferred over those query-based approaches, leaving the agent without context for selection.

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

activitywatch_get_settingsB

Get ActivityWatch settings. Can retrieve all settings or a specific key if provided.

ParametersJSON Schema

Name	Required	Description	Default
`key`	No	Optional: Get a specific settings key instead of all settings

TDQS

B3.2/5.0

Behavior2/5

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 states the tool retrieves settings but doesn't disclose behavioral traits such as whether it's read-only (implied by 'Get'), authentication needs, rate limits, error handling, or response format. The description is minimal and lacks necessary context for safe and effective use.

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 appropriately sized with two concise sentences that are front-loaded and waste-free. It efficiently communicates the core functionality and parameter usage without unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what settings are returned, their structure, or any behavioral aspects like permissions or errors. For a tool with zero annotation coverage, more context is needed for adequate agent understanding.

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 the schema already documents the optional 'key' parameter. The description adds marginal value by mentioning 'a specific key if provided,' but doesn't provide additional semantics like key examples, format, or constraints beyond what the schema states.

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 clearly states the tool's purpose with a specific verb ('Get') and resource ('ActivityWatch settings'), and distinguishes between retrieving all settings or a specific key. However, it doesn't explicitly differentiate from sibling tools like 'activitywatch_get_events' or 'activitywatch_list_buckets' beyond the resource name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning 'all settings or a specific key if provided,' which suggests when to use the optional parameter. However, it lacks explicit guidance on when to choose this tool over siblings (e.g., vs. 'activitywatch_get_events') or any prerequisites or exclusions.

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

activitywatch_list_bucketsB

List all ActivityWatch buckets with optional type filtering

ParametersJSON Schema

Name	Required	Description	Default
`type`	No	Filter buckets by type
`includeData`	No	Include bucket data in response

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a list operation with optional filtering, implying it's likely read-only and non-destructive, but doesn't confirm this or address other behavioral aspects like permissions needed, rate limits, pagination, error handling, or what 'includeData' entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose ('List all ActivityWatch buckets') and adds a concise modifier ('with optional type filtering'). There is no wasted verbiage, repetition, or unnecessary elaboration, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic action and hints at filtering, but lacks details on behavioral traits, usage context, or output expectations. Without annotations or output schema, the agent must infer behavior from the description alone, which is insufficient for confident tool selection in varied scenarios.

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 the schema already documents both parameters ('type' for filtering and 'includeData' for including bucket data). The description adds minimal value by mentioning 'optional type filtering,' which aligns with the schema but doesn't provide additional context like example types or implications of including data. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding beyond what's structured.

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 clearly states the action ('List all ActivityWatch buckets') and resource ('ActivityWatch buckets'), making the purpose immediately understandable. It distinguishes itself from siblings like 'activitywatch_get_events' by focusing on buckets rather than events, though it doesn't explicitly contrast with other bucket-related tools (none exist in the sibling list). The optional filtering aspect adds specificity beyond a basic list operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 mentions optional type filtering but doesn't explain scenarios where filtering is beneficial or when to choose this over other tools like 'activitywatch_query_examples' or 'activitywatch_run_query' for bucket-related tasks. There are no prerequisites, exclusions, or contextual recommendations provided.

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

activitywatch_query_examplesB

Get examples of properly formatted queries for the ActivityWatch MCP server

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets examples,' implying a read-only operation, but doesn't clarify if this requires authentication, has rate limits, returns structured data, or involves any side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 a single, clear sentence: 'Get examples of properly formatted queries for the ActivityWatch MCP server.' It is front-loaded with the core action ('Get examples') and specifies the context ('for the ActivityWatch MCP server'), with zero wasted words. This makes it highly efficient and easy for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does but lacks behavioral details (e.g., response format, authentication needs) and usage guidelines relative to siblings. Without an output schema, it doesn't describe return values, which could hinder an agent's understanding of how to use the examples provided.

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 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, as there are none. It appropriately focuses on the tool's purpose without redundant parameter details, earning a high baseline score for parameter semantics in this context.

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 clearly states the tool's purpose: 'Get examples of properly formatted queries for the ActivityWatch MCP server.' It uses a specific verb ('Get') and identifies the resource ('examples of properly formatted queries'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'activitywatch_run_query' or 'activitywatch_get_events,' which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 doesn't mention prerequisites (e.g., needing to understand query syntax before using 'activitywatch_run_query'), exclusions, or contextual cues. Without such information, an agent might struggle to choose between this and sibling tools like 'activitywatch_run_query' or 'activitywatch_get_events.'

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

activitywatch_run_queryC

Run a query in ActivityWatch's query language

ParametersJSON Schema

Name	Required	Description
`timeperiods`	Yes	Time periods to query. Format: ['2024-10-28/2024-10-29'] where dates are in ISO format and joined with a slash
`query`	Yes	MUST BE A SINGLE STRING containing all query statements separated by semicolons. DO NOT split into multiple strings.
`name`	No	Optional name for the query (used for caching)

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic action. It doesn't mention whether this is a read-only operation, if it requires specific permissions, potential side effects, rate limits, or what the output looks like (especially critical since there's no output schema). This leaves significant gaps for a query execution tool.

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 a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a tool with well-documented parameters and gets straight to the point, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a query execution tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, error conditions, or behavioral constraints. Given the complexity of running queries and the lack of structured output documentation, more context about the operation's nature and results is needed.

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?

The description adds no parameter information beyond what's already in the schema (which has 100% coverage). While the schema thoroughly documents all three parameters with formats, constraints, and examples, the description doesn't provide additional context about parameter relationships or usage semantics, meeting the baseline for high schema coverage.

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 clearly states the action ('Run a query') and the resource ('in ActivityWatch's query language'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from its sibling 'activitywatch_query_examples', which might cause confusion about when to use each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like 'activitywatch_get_events' or 'activitywatch_query_examples'. There's no mention of prerequisites, appropriate contexts, or exclusions, leaving the agent to infer usage from the tool name alone.

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 updatesv1.0.0Jan 2, 2026
- Addedactivitywatch_get_events
- Addedactivitywatch_get_settings
- Addedactivitywatch_list_buckets
- Addedactivitywatch_query_examples
- Addedactivitywatch_run_query

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

A3.5/5.0

Scored 2026-04-18 11:35 across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: get_events retrieves raw event data, get_settings accesses configuration, list_buckets enumerates available data containers, query_examples provides documentation, and run_query executes queries. There is no overlap in functionality that would cause agent confusion.

Naming Consistency5/5

All tools follow a consistent 'activitywatch_verb_noun' pattern with snake_case throughout. The verbs (get, list, run) are appropriately descriptive and maintain a uniform naming convention across all five tools.

Tool Count5/5

With 5 tools, this server is well-scoped for interacting with ActivityWatch data. The count is appropriate for the domain, covering core operations like data retrieval, configuration access, and query execution without being overwhelming or insufficient.

Completeness4/5

The toolset provides solid coverage for querying and inspecting ActivityWatch data, including examples for guidance. A minor gap exists in write operations (e.g., creating or modifying buckets/events), but the available tools support most common read and query workflows effectively.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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MCP-сервер ActivityWatch

Возможности

Установка

Установка через npm (скоро)

Сборка из исходного кода

Предварительные требования

Использование

Использование с Claude for Desktop

Использование rootless-контейнера podman в Linux с Gemini CLI

Переопределение хоста/порта сервера ActivityWatch

Примеры запросов

Доступные инструменты

list-buckets

run-query

get-events

get-settings

Примеры языка запросов

Конфигурация

Устранение неполадок

ActivityWatch не запущен

Ошибки запросов

Проблемы с форматированием запросов Claude/MCP

Самая распространенная проблема форматирования

При составлении промптов для Claude

Вклад в проект

Лицензия

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

TripleWhale MCP Serverofficial

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