colab-mcp
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 |
|---|---|
| tasks | {
"list": {},
"cancel": {},
"requests": {
"tools": {
"call": {}
},
"prompts": {
"get": {}
},
"resources": {
"read": {}
}
}
} |
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| open_colab_browser_connectionA | Checks whether a Google Colab proxy session is already connected. Returns a boolean representing whether the headless proxy connection is available. |
| connect_colabD | – |
| list_colab_cellsD | – |
| read_colab_cellD | – |
| write_colab_cellD | – |
| run_colab_cellD | – |
| run_colab_codeD | – |
| get_colab_outputD | – |
| save_colab_notebookD | – |
| run_runtime_codeD | – |
| setup_ml_workspaceD | – |
| fetch_remote_datasetD | – |
| execute_ml_pipelineD | – |
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 13 tools
Several tools appear to serve similar purposes: run_colab_cell, run_colab_code, run_runtime_code, and execute_ml_pipeline all seem to execute code, while connect_colab and open_colab_browser_connection overlap in establishing/checking a connection. Without descriptions, it's hard for an agent to pick the right tool.
Most tools follow a verb_noun snake_case pattern, but there's inconsistency: some include 'colab' (e.g., list_colab_cells) while others do not (e.g., setup_ml_workspace, fetch_remote_dataset). Also, the verbs vary widely, making the naming feel less systematic than a strict verb_noun convention.
13 tools is within a reasonable range for a Colab-focused server. However, the presence of four overlapping execution tools inflates the count and suggests some consolidation could improve the set without losing functionality.
The tool surface covers core cell operations (read, write, run, output) and notebook saving, but lacks obvious lifecycle operations like deleting a cell or creating a notebook. The ML-related tools (setup workspace, fetch dataset, pipeline) add breadth but also introduce unclear boundaries and gaps, such as no explicit tool for managing datasets beyond fetching.