Run model-export code in a Colab runtime to convert models to ONNX or TorchScript and register the resulting artifact for downstream use.
Run model-export code (e.g. ONNX/TorchScript) in a runtime session and register the exported artifact.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| name | No | ||
| job_id | No | ||
| session_id | No | ||
| artifact_path | No | ||
| timeout_seconds | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that code runs in a session and an artifact is registered, but it does not mention required setup, existing session expectations, overwrite behavior, timeout effects, or the arbitrary side-effect potential of running user-supplied code.
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 concrete examples and no wasted words. It efficiently communicates the core purpose.
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?
With 6 undocumented parameters, no output schema, and no annotations, this one-line description is not enough to invoke the tool correctly. It gives a good summary but omits parameter semantics, usage boundaries, and behavioral expectations.
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 adds only a weak mapping: 'code' is model-export code and the output is an 'exported artifact'. The meaning and role of name, job_id, session_id, artifact_path, and timeout_seconds remain unexplained, so the agent must guess.
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 clear, specific action: run model-export code such as ONNX/TorchScript in a runtime session and register the exported artifact. This distinguishes it from notebook export and generic code execution by emphasizing artifact registration for model exports.
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 intended use is implied by the phrase 'model-export code', but there is no explicit guidance about when to use this tool versus alternatives like colab_execute_code or colab_export_notebook. Clear context is present, but exclusions and alternative routing are not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.