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set_env_vars

Set environment variables in a Colab notebook runtime. Optionally keep the generated setup cell for later reuse.

Instructions

Sets environment variables in the current Colab Python runtime by executing a generated setup cell.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variablesYesEnvironment variable names and values to set.
persistNoIf true, keep the generated setup cell in the notebook. If false, delete it after execution while leaving the runtime environment variables set.
markerNameNoMarker name used to find this setup cell later with rerun_env_setup_cells.default
cellIndexNoOptional insertion index for the generated setup cell. Defaults to the end of the notebook.
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It does not disclose side effects like generating/deleting cells, execution context, or impact on notebook state. The persist and markerName parameters are not mentioned in the description, leaving behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is concise and front-loaded with purpose. However, it could include more context without becoming verbose, e.g., mention of the generated setup cell or persistence behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 parameters, no output schema, no annotations), the description is too brief. It does not explain how the setup cell works, error conditions, or the effect of parameters like persist and markerName. More context is needed for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what the schema provides; it only restates the overall purpose. It does not elaborate on parameter usage or relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'sets' and the resource 'environment variables' in a specific context ('current Colab Python runtime') and mechanism ('executing a generated setup cell'). This distinguishes it from siblings like get_env_vars, unset_env_vars, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as set_env_vars vs. load_env_file or rerun_env_setup_cells. The description does not mention prerequisites, context of use, or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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