mcp-server-colab-exec
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| colab_executeA | Execute Python code on a Google Colab GPU runtime. Allocates a T4 or L4 GPU, runs the code, and returns structured JSON with per-cell output, errors, and stderr. Args: code: Python code to execute on the Colab GPU runtime. accelerator: GPU type — "T4" (free-tier) or "L4" (premium). Default: "T4". timeout: Max execution time in seconds. Default: 300. |
| colab_execute_fileA | Execute a local Python file on a Google Colab GPU runtime. Reads the file contents and sends them for execution on a Colab GPU. Args: file_path: Path to a local .py file to execute on Colab. accelerator: GPU type — "T4" (free-tier) or "L4" (premium). Default: "T4". timeout: Max execution time in seconds. Default: 300. |
| colab_execute_notebookA | Execute Python code on Colab GPU and collect generated artifacts. Runs the code, then scans the runtime for output files (images, CSVs, models, etc.), zips them, and downloads to a local directory. Args: code: Python code to execute on the Colab GPU runtime. output_dir: Local directory to save the artifacts zip and extracted files. accelerator: GPU type — "T4" (free-tier) or "L4" (premium). Default: "T4". timeout: Max execution time in seconds. Default: 300. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Tools serve distinct use cases: inline code, file execution, and artifact collection. However, colab_execute and colab_execute_notebook both take 'code' parameter, risking slight confusion if descriptions aren't read carefully.
All tools follow a consistent 'colab_execute_<action>' pattern using snake_case. The naming is predictable and clear.
Three tools cover the core action of executing code on Colab. The count is slightly low but each tool provides distinct functionality, making it appropriate for the focused scope.
The set covers code execution and artifact retrieval, but lacks tools for managing runtimes (e.g., list, stop) or retrieving outputs separately. This creates minor operational gaps for agents.