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restore_pipeline_version

Restores a specific version of a pipeline, setting it as the active configuration.

Instructions

Restores a pipeline to a previous version, making that version the active configuration. :param pipeline_name: Name of the pipeline to restore. :param version_id: UUID of the version to restore. :returns: The restored pipeline version or error message.

All parameters accept object references in the form @obj_id or @obj_id.path.to.value.

Examples::

# Direct call with values
restore_pipeline_version(data={'key': 'value'}, threshold=10)

# Call with references
restore_pipeline_version(data='@obj_123', threshold='@obj_456.config.threshold')

# Mixed call
restore_pipeline_version(data='@obj_123.items', threshold=10)The output is automatically stored and can be referenced in other functions.

Returns a formatted preview with an object ID (e.g., @obj_123). Use the object store tools in combination with the object ID to view nested properties of the object. Use the returned object ID to pass this result to other functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pipeline_nameYes
version_idYes
Behavior3/5

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

Without annotations, the description must carry behavioral transparency. It states that the tool 'makes that version the active configuration' and that 'The output is automatically stored and can be referenced in other functions.' This indicates mutation and side effects, but it does not clarify if the operation is destructive, reversible, or requires permissions.

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

Conciseness2/5

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

The description is verbose and includes Python docstring syntax (:param:, :returns:) and examples that could be streamlined. The structure is not optimized for an AI agent, as it mixes multiple sections (docstring, examples, object store notes) without clear prioritization.

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

Completeness4/5

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

Given the tool's simplicity (2 required parameters, no output schema), the description covers the core behavior and output handling. It explains what the tool does, what the parameters mean, and how the result can be used. However, it omits potential error conditions or prerequisites.

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?

The input schema has no property descriptions (0% coverage), but the description includes docstring-like parameter comments: 'Name of the pipeline to restore' and 'UUID of the version to restore.' This adds basic semantics beyond the schema, confirming the role of each parameter, though the information is minimal.

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 that the tool 'Restores a pipeline to a previous version, making that version the active configuration.' This provides a specific verb and resource, distinguishing it from sibling tools like get_pipeline_version (read-only) and patch_pipeline_version (update current version).

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

Usage Guidelines3/5

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

The description includes examples showing direct calls and object references, implying common usage patterns. However, it does not explicitly state when to use this tool versus alternatives like deploy_pipeline or patch_pipeline_version, nor does it mention 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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