Save trained models by executing your model-saving code in a Colab runtime, with optional artifact registration for later retrieval.
Run model-saving code in a runtime session, then (optionally) register the resulting file under a job's artifacts via colab_get_artifacts.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| name | No | ||
| job_id | No | ||
| session_id | No | ||
| artifact_path | No | Path (relative to the artifact root) the code saved to. | |
| timeout_seconds | No |
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 full burden of behavioral disclosure. It reveals that code is run in a runtime session and artifact registration is optional, but it does not mention side effects, overwrite behavior, authentication, failure modes, or what happens when artifact_path is omitted.
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?
A single sentence that front-loads the action, states the optional registration behavior, and includes a reference to the relevant sibling tool. There is no filler or redundant restatement of the schema.
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?
The tool has six parameters, no annotations, and no output schema, yet the description is only one high-level sentence. It does not explain session/job relationships, required versus optional parameters, timeout defaults, or expected return behavior, so an agent cannot reliably invoke it correctly.
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 only 17%, and the description adds little parameter meaning: it implies code contains model-saving logic and artifact_path is where the resulting file is stored, but it does not clarify name, job_id, session_id, or timeout_seconds.
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 action ('Run model-saving code in a runtime session') and a concrete outcome ('register the resulting file under a job's artifacts'), which makes the tool's purpose clear and distinguishes it from generic execution tools like colab_execute_code and artifact-related tools like colab_get_artifacts.
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 phrase 'model-saving code' implies this is for persisting models and optionally registering outputs, but the description never explicitly states when to choose it over colab_execute_code or when to skip artifact registration. There is no when-not-to-use guidance or named alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.