Colab MCP
🪐 Colab MCP (Model Context Protocol)
로컬 AI 에이전트를 브라우저에서 실행 중인 Google Colab 세션에 원활하게 연결하는 MCP (Model Context Protocol) 서버입니다.
✨ 주요 기능
로컬 AI 어시스턴트를 브라우저 기반 Colab 노트북에 직접 연결
에이전트를 통해 Colab에서 Python 코드 실행 지원
Colab 노트북 상태 읽기 및 상호 작용
Related MCP server: colab-mcp
💻 지원되는 클라이언트
이 MCP 서버는 notifications/tools/list_changed를 지원하는 클라이언트가 필요하며, 사용자의 기기에서 로컬로 실행되어야 합니다.
이 기준을 충족하는 주요 클라이언트는 다음과 같습니다:
🚀 설치 및 설정
uv설치 (매우 빠른 Python 패키지 설치 및 해결 도구):pip install uvMCP 클라이언트 구성 (
mcp.json또는 이에 상응하는 구성 파일):{ "mcpServers": { "colab-mcp": { "command": "uvx", "args": ["git+https://github.com/googlecolab/colab-mcp"], "timeout": 30000 } } }Googler(또는 비표준 패키지 인덱스 사용자)를 위한 참고 사항:
args배열에--index https://pypi.org/simple을 추가해야 할 수도 있습니다.
💬 문제 및 토론
GitHub Discussions를 문제 토론 및 기능 요청을 위한 주요 창구로 사용하고 있습니다.
토론이 명확한 실행 항목으로 발전하면 관리자가 이를 추적 가능한 이슈로 변환합니다. 이 워크플로우는 이슈 트래커가 중복되지 않고, 명확하게 이해되며, 실행 가능한 상태로 유지되도록 돕습니다.
⚠️ 이슈를 직접 생성하지 마십시오.
🤝 기여
커뮤니티의 관심은 감사하지만, 현재 외부 기여를 검토할 여력이 없습니다. 사용자 Pull Request가 검토 없이 방치되는 것을 원치 않으므로 현재 외부 기여는 받지 않고 있습니다.
좋은 아이디어가 있거나 불편한 점이 있다면 Discussions 페이지에서 의견을 공유해 주세요!
🛠️ 내부용 (Colab 개발자용)
사전 요구 사항
uv가 필요합니다 (pip install uv)저장소 사전 제출(presubmit)을 실행하도록 git 훅을 구성하세요:
git config core.hooksPath .githooks
로컬 개발 설정 (Gemini CLI)
Gemini CLI로 로컬 체크아웃을 테스트하려면 다음 구성을 사용하세요:
{
"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.
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