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🪐 Colab MCP (模型上下文协议)

Python MCP

一个 MCP (模型上下文协议) 服务器,可将您的本地 AI 智能体无缝连接到浏览器中运行的 Google Colab 会话。

✨ 功能

  • 将本地 AI 助手直接连接到基于浏览器的 Colab 笔记本

  • 支持通过智能体在 Colab 中执行 Python 代码

  • 读取并与 Colab 笔记本状态进行交互

Related MCP server: colab-mcp

💻 支持的客户端

此 MCP 服务器需要一个支持 notifications/tools/list_changed 且必须在您的设备上本地运行的客户端。

符合这些条件的常用客户端包括:

🚀 安装与设置

  1. 安装 uv (一个速度极快的 Python 包安装程序和解析器):

    pip install uv
  2. 配置您的 MCP 客户端 (例如,在您的 mcp.json 或等效配置文件中):

    {
      "mcpServers": {
        "colab-mcp": {
          "command": "uvx",
          "args": ["git+https://github.com/googlecolab/colab-mcp"],
          "timeout": 30000
        }
      }
    }

    谷歌员工(或使用非标准包索引的用户)请注意: 您可能需要在 args 数组中添加 --index https://pypi.org/simple。

💬 问题与讨论

我们使用 GitHub Discussions 作为我们讨论问题和功能请求的主要场所。

当讨论成熟为明确的行动项时,维护者会将其转换为跟踪问题。此工作流程有助于我们确保问题跟踪器保持去重、易于理解且具有高度可操作性。

⚠️ 请不要直接提交 issue。

🤝 贡献

虽然我们感谢社区的关注,但我们目前没有足够的带宽来审查外部贡献。我们希望避免用户的 Pull Request 因缺乏审查而搁置,因此我们目前不接受外部贡献。

如果您有好的想法或遇到痛点,我们非常乐意在我们的 Discussions 页面上听到您的声音!


🛠️ 内部(供 Colab 开发人员使用)

先决条件

  • 需要 uv (pip install uv)

  • 配置 git 钩子以运行存储库预提交检查:

    git config core.hooksPath .githooks

本地开发设置 (Gemini CLI)

要使用 Gemini CLI 测试您的本地检出版本,请使用以下配置:

{
  "mcpServers": {
    "colab-mcp": {
      "command": "uv",
      "args": ["run", "colab-mcp"],
      "cwd": "/path/to/github/colab-mcp",
      "timeout": 30000
    }
  }
}

MCP_Colab

Available Tools

1 tool
open_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

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4/5.0
Behavior3/5

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 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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. 1 tool updatev1.0.1
    • First observedopen_colab_browser_connection

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation1/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness1/5

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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