Colab
The Colab-mcp MCP Server provides a direct interface between MCP-compatible clients and Google Colab environments.
It allows you to execute Python code, manage notebooks, and leverage Colab’s computational resources through the Model Context Protocol.
This MCP server turns your AI assistant into a capable data science tool that can run analyses, train models, and generate visualizations in a Colab runtime.
How To Use It
1. Make sure you first have uv package manager installed.
pip install uv2. Configure your MCP client to use the server. Add the following configuration to your mcp.json file or equivalent service configuration.
"mcpServers": {
"colab-mcp": {
"command": "uvx",
"args":["git+https://github.com/googlecolab/colab-mcp"],
"timeout": 30000
}
}3. Googlers and users with a non-standard default package index must modify the arguments. Append --index https://pypi.org/simple to the args array. This forces the tool to use the standard PyPI index.
4. Colab developers must follow a specific internal setup process. Install uv using the pip command shown above. Configure git hooks to run repository presubmits. Run the following command in your terminal.
git config core.hooksPath .githooksConfigure the Gemini CLI using the local repository path. Add the following configuration to your mcp.json file. I use this specific block when testing local changes.
"mcpServers": {
"colab-mcp": {
"command": "uv",
"args": ["run", "colab-mcp"],
"cwd": "/path/to/github/colab-mcp",
"timeout": 30000
}
}FAQs
Q: What is uv and why do I need it?
A: uv is an extremely fast Python package installer and resolver, written in Rust. Colab-mcp uses it to run the server directly from the Git repository.
Q: Which MCP clients are compatible with Colab-mcp?
A: Any client that supports the Model Context Protocol and allows custom server configuration can use Colab-mcp.
Q: How do I handle authentication errors?
A: Authentication errors usually occur if the OAuth flow was interrupted or if your session expired. Restart your MCP client and try the operation again. The server will prompt you to re‑authenticate.
Q: Can I use Colab-mcp with multiple Google accounts?
A: The server stores credentials for the account you authenticated with. To switch accounts, you need to clear the stored tokens.
Q: What happens if my Colab runtime disconnects?
A: Colab runtimes can disconnect after periods of inactivity. The server will detect this and attempt to reconnect automatically when you make the next tool call. If reconnection fails, you need to start a new session.
Q: Can I use Colab‑mcp with a Colab Pro or Colab Pay‑As‑You‑Go account?
A: Yes, the server uses your standard Colab authentication, so it respects your account type and its associated benefits (like better GPUs or longer runtimes).
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FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
Q: Is MCP secure? What are the main risks?
A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.
