Colab MCP
🪐 Colab MCP (Protocolo de Contexto de Modelo)
Un servidor MCP (Protocolo de Contexto de Modelo) que conecta sin problemas tu agente de IA local con una sesión de Google Colab ejecutándose en tu navegador.
✨ Características
Conecta asistentes de IA locales directamente a cuadernos de Colab basados en navegador
Admite la ejecución de código Python en Colab a través del agente
Lee e interactúa con los estados de los cuadernos de Colab
Related MCP server: colab-mcp
💻 Clientes compatibles
Este servidor MCP requiere un cliente que admita notifications/tools/list_changed y debe ejecutarse localmente en tu dispositivo.
Los clientes populares que cumplen con estos criterios incluyen:
🚀 Instalación y configuración
Instala
uv(un instalador y resolvedor de paquetes de Python extremadamente rápido):pip install uvConfigura tu cliente MCP (por ejemplo, en tu
mcp.jsono archivo de configuración equivalente):{ "mcpServers": { "colab-mcp": { "command": "uvx", "args": ["git+https://github.com/googlecolab/colab-mcp"], "timeout": 30000 } } }Nota para empleados de Google (o aquellos con índices de paquetes no estándar): Es posible que necesites añadir
--index https://pypi.org/simplea la matrizargs.
💬 Problemas y debates
Utilizamos los Debates de GitHub como nuestro lugar principal para la discusión de problemas y solicitudes de funciones.
A medida que los debates se conviertan en elementos de acción claros, los mantenedores los convertirán en problemas rastreados. Este flujo de trabajo nos ayuda a garantizar que el rastreador de problemas permanezca sin duplicados, bien comprendido y altamente procesable.
⚠️ Por favor, NO abras problemas directamente.
🤝 Contribuciones
Aunque agradecemos el interés de la comunidad, actualmente no tenemos la capacidad para revisar contribuciones externas. Queremos evitar que las solicitudes de extracción (Pull Requests) de los usuarios languidezcan sin revisión, por lo que no estamos aceptando contribuciones externas en este momento.
Si tienes una gran idea o encuentras un punto de dolor, ¡nos encantaría saberlo en nuestra página de Debates!
🛠️ Interno (Para desarrolladores de Colab)
Requisitos previos
Se requiere
uv(pip install uv)Configura los hooks de git para ejecutar las comprobaciones previas del repositorio:
git config core.hooksPath .githooks
Configuración de desarrollo local (Gemini CLI)
Para probar tu copia local con Gemini CLI, utiliza esta configuración:
{
"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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