Colab MCP
🪐 Colab MCP (Model Context Protocol)
Сервер MCP (Model Context Protocol), который бесшовно связывает вашего локального ИИ-агента с сеансом Google Colab, запущенным в браузере.
✨ Возможности
Подключает локальных ИИ-ассистентов напрямую к блокнотам Colab в браузере
Поддерживает выполнение кода Python в Colab через агента
Читает состояние блокнота Colab и взаимодействует с ним
Related MCP server: colab-mcp
💻 Поддерживаемые клиенты
Для работы этого MCP-сервера требуется клиент, поддерживающий notifications/tools/list_changed, который должен быть запущен локально на вашем устройстве.
Популярные клиенты, соответствующие этим критериям:
🚀 Установка и настройка
Установите
uv(чрезвычайно быстрый установщик и резолвер пакетов Python):pip install uvНастройте ваш MCP-клиент (например, в файле
mcp.jsonили аналогичном конфигурационном файле):{ "mcpServers": { "colab-mcp": { "command": "uvx", "args": ["git+https://github.com/googlecolab/colab-mcp"], "timeout": 30000 } } }Примечание для сотрудников Google (или тех, кто использует нестандартные индексы пакетов): Возможно, вам потребуется добавить
--index https://pypi.org/simpleв массивargs.
💬 Проблемы и обсуждения
Мы используем Discussions на GitHub в качестве основной площадки для обсуждения проблем и запросов на добавление функций.
По мере того как обсуждения перерастают в четкие задачи, сопровождающие будут преобразовывать их в отслеживаемые проблемы (issues). Этот рабочий процесс помогает нам гарантировать, что трекер проблем остается свободным от дубликатов, понятным и максимально ориентированным на действия.
⚠️ Пожалуйста, НЕ открывайте issues напрямую.
🤝 Участие в разработке
Хотя мы ценим интерес сообщества, в настоящее время у нас нет ресурсов для проверки внешних вкладов. Мы хотим избежать ситуации, когда Pull Requests от пользователей остаются без внимания, поэтому в данный момент мы не принимаем внешние вклады.
Если у вас есть отличная идея или вы столкнулись с проблемой, мы будем рады узнать об этом на нашей странице Discussions!
🛠️ Внутренняя информация (для разработчиков Colab)
Предварительные требования
Требуется
uv(pip install uv)Настройте git-хуки для запуска предварительных проверок репозитория:
git config core.hooksPath .githooks
Настройка локальной разработки (Gemini CLI)
Чтобы протестировать вашу локальную версию с помощью Gemini CLI, используйте следующую конфигурацию:
{
"mcpServers": {
"colab-mcp": {
"command": "uv",
"args": ["run", "colab-mcp"],
"cwd": "/path/to/github/colab-mcp",
"timeout": 30000
}
}
}MCP_Colab
Available Tools
1 toolopen_colab_browser_connectionA
Opens a connection to a Google Colab browser session and unlocks notebook editing tools. Returns a boolean representing whether the connection attempt succeeded
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 of behavioral disclosure. It mentions the side effect of unlocking editing tools and the boolean return value, but it does not clarify whether the connection is persistent, whether it requires an existing browser session, or whether any side effects beyond unlocking occur.
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 a single, front-loaded sentence that conveys the action, the target resource, the functional outcome, and the return type. There is no redundant or filler content.
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 zero-parameter tool with an output schema, the description is nearly complete: it names the action and return value. The main gap is the lack of any prerequisite or failure-context information, such as requiring an active Colab browser session or what happens if the connection attempt fails.
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 tool has zero parameters, so the schema provides no parameter semantics to clarify. The description does not need to add parameter meaning, and it appropriately focuses on the operation and return value.
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 uses a specific verb ('Opens') and names a clear resource ('a Google Colab browser session'), and also states the expected outcome ('unlocks notebook editing tools'). With no sibling tools to differentiate, this fully communicates what the tool does.
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 the tool is used when a Colab browser connection is needed, but it does not state explicit when-to-use guidance, prerequisites, or alternatives. Since there are no sibling tools, the lack of exclusions is acceptable, but contextual guidance is minimal.
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 tool update
v1.0.1- First observed
open_colab_browser_connection
TDQS
Scored across 1 tool
Only one tool exists, so no ambiguity between tools, but the dimension assesses whether tools can be told apart; with one tool there is no need for disambiguation, but it cannot be 'clearly distinct' from others since there are none.
With a single tool, naming consistency is not applicable; however, the name is descriptive and follows a reasonable pattern, so a neutral score is given.
A single tool seems too few for a server named 'Colab MCP', which suggests a broader purpose. The tool only handles opening a connection, leaving other expected functionalities uncovered.
The server's domain appears to be Google Colab integration, but only one tool for opening a connection is provided. Missing tools for editing, running cells, managing notebooks, etc., make the surface severely incomplete.
Related MCP Connectors
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
Remote MCP server for The Colony — a social network for AI agents (posts, DMs, search, marketplace).
An MCP server that gives your AI access to the source code and docs of all public github repos
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
Related MCP Servers
- AlicenseBqualityDmaintenanceLocal-first MCP server for controlling Google Colab as a development, shell, file, and training runtime, with tools for notebook editing, GPU acceleration, and file transfer.598Apache 2.0
- Apache 2.0
- AlicenseNot gradedqualityDmaintenanceMCP server that connects AI agents to Google NotebookLM, enabling natural language interaction with notebooks, including Q&A, source ingestion, and audio overview generation.2,297 npmMIT
- AlicenseAqualityDmaintenanceAn MCP server for bridging your local agent to a Colab session in the browser.1Apache 2.0