colab_get_notebook
Fetch a notebook's cells and metadata from its file path to inspect code, outputs, and configuration.
Instructions
Read a notebook's cells and metadata.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Fetch a notebook's cells and metadata from its file path to inspect code, outputs, and configuration.
Read a notebook's cells and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. The verb 'Read' implies a non-destructive, side-effect-free operation, which is useful. However, the description does not address whether this reads from disk, session state, or APIs, nor does it describe pagination, freshness, or the structure of the returned metadata. It is adequate for a simple getter but not fully transparent.
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 with no filler. It efficiently conveys the core purpose and scope in very few words, which is ideal for quick agent scanning.
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 tool with one required parameter and no output schema, the description gives a minimal but workable overview: it names the operation and the intended result. However, it lacks critical contextual guidance such as how 'path' should be specified, what the returned metadata looks like, and how this differs from colab_read_file or colab_get_job in practice. It is adequate but leaves gaps.
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?
Schema description coverage is 0% and the description does not explain the meaning or format of the required 'path' parameter. The word 'notebook' in the description implies path identifies a notebook, but whether it is a workspace path, notebook ID, or remote URI is left unspecified. With no schema description to compensate, the agent must guess at valid path semantics.
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 states a specific action ('Read') and a specific resource ('a notebook's cells and metadata'), which clearly distinguishes this tool from sibling tools like colab_create_notebook, colab_update_notebook, and colab_export_notebook. The scope of what is returned is made explicit, so an agent can identify this tool's purpose without ambiguity.
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 usage through its purpose ('read when you need a notebook's cells and metadata'), but it does not explicitly state when to prefer this over closely related tools such as colab_read_file, colab_get_logs, or colab_export_notbook. No when-not or alternative-guidance is provided, leaving some inference required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.