Skip to main content
Glama

restore_pipeline_version

Restore a pipeline to a previous version using its name and version UUID, making that version 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?

With no annotations, the description carries the full burden. It explains the action (restore, make active), parameter object references, and return format (preview with object ID). However, it does not disclose potential side effects like overwriting current active version, permissions required, or error cases.

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 structured with param docs and examples, but it is somewhat verbose with repeated mentions of object references. The examples are helpful but could be more succinct. Overall, it is well-organized for readability.

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 no annotations or output schema, the description covers purpose, parameters, object reference feature, return format, and storage of output. It lacks error handling details and differentiation from similar tools, but is largely complete for a two-parameter function.

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

Parameters4/5

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

The schema provides only param names and types, but the description adds meaning by explaining each parameter ('Name of the pipeline to restore', 'UUID of the version to restore') and the object reference feature. This compensates for the 0% schema description coverage.

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 tool restores a pipeline to a previous version and makes it active. The verb 'restores' and resource 'pipeline version' are specific, and the outcome 'active configuration' distinguishes it from sibling tools like get_pipeline_version or patch_pipeline_version.

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?

The description does not provide explicit guidelines on when to use this tool versus alternatives (e.g., patch_pipeline_version, deploy_pipeline). It implies the usage context (restoring a previous version) but lacks when-not-to-use or alternative suggestions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/deepset-ai/deepset-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server