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aerovfx

Colab MCP

by aerovfx
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

🪐 Colab MCP (Model Context Protocol)

Python MCP

An MCP (Model Context Protocol) server that seamlessly bridges your local AI agent to a Google Colab session running in your browser.

✨ Features

  • Connects local AI assistants directly to browser-based Colab notebooks

  • Supports executing Python code in Colab via the agent

  • Reads and interacts with Colab notebook states

Related MCP server: colab-mcp

💻 Supported Clients

This MCP server requires a client that supports notifications/tools/list_changed and must be running locally on your device.

Popular clients that meet these criteria include:

🚀 Installation & Setup

  1. Install uv (an extremely fast Python package installer and resolver):

    pip install uv

  2. Configure your MCP Client (e.g., in your mcp.json or equivalent configuration file):

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

    Note for Googlers (or those with non-standard package indexes): You may need to add --index https://pypi.org/simple to the args array.

💬 Issues & Discussions

We use GitHub Discussions as our primary venue for issue discussion and feature requests.

As discussions mature into clear action items, the maintainers will convert them into tracked issues. This workflow helps us ensure that the issue tracker remains deduplicated, well-understood, and highly actionable.

⚠️ Please do NOT open issues directly.

🤝 Contributing

While we appreciate community interest, we currently do not have the bandwidth to review external contributions. We want to avoid user Pull Requests languishing without review, so we are not accepting external contributions at this time.

If you have a great idea or encounter a pain point, we would love to hear about it on our Discussions page!

🛠️ Internal (For Colab Developers)

Prerequisites

  • uv is required (pip install uv)

  • Configure git hooks to run repository presubmits:

    git config core.hooksPath .githooks

Local Development Setup (Gemini CLI)

To test your local checkout with the Gemini CLI, use this configuration:

{
  "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

A3.6/5.0
Behavior3/5

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

With no annotations, the description must fully disclose behavior. It states the outcome (connection success) and return type (boolean), but lacks details on side effects, prerequisites (e.g., must be in Colab environment), or what 'unlocking' entails.

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 concise, two sentences, front-loaded with the action and purpose. No wasted words.

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 zero parameters and a simple return type, the description is mostly complete. However, it could mention prerequisites or environment requirements to fully prepare the agent.

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?

Schema has no parameters, so description does not need to elaborate. The description adds value by explaining the boolean return meaning.

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 it opens a connection to a Google Colab browser session and unlocks notebook editing tools, and it returns a boolean for success. This is specific and actionable.

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 when to use it (when needing to access Colab notebook editing), but does not explicitly state when not to use it or provide alternatives. With no siblings, this is acceptable but could be more precise.

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. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.1
    • First observedopen_colab_browser_connection
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TDQS

B3.1/5.0
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.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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