Skip to main content
Glama

create_pipeline_version

Creates a new immutable version of a pipeline with provided YAML configuration, preserving the full history of changes.

Instructions

Creates a new version of an existing pipeline with the provided YAML configuration.

Use this to update a pipeline's configuration. Each call creates a new immutable version, preserving the full history of changes. :param pipeline_name: Name of the pipeline to create a version for. :param yaml_configuration: The new YAML configuration for this version. :param description: Optional description of what changed in this version. :param is_draft: If True, the version is created as a draft (default: False). :returns: The newly created 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
create_pipeline_version(data={'key': 'value'}, threshold=10)

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

# Mixed call
create_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
yaml_configurationYes
descriptionNo
is_draftNo
Behavior5/5

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

No annotations provided, so the description fully bears the burden. It discloses immutability ('new immutable version'), history preservation ('preserving full history'), output behavior ('returns formatted preview with object ID'), and storage ('output is automatically stored'). This is thorough.

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 well-structured with purpose, usage, parameters, references, examples, and output info. It's front-loaded and not overly verbose, though could be slightly tightened. Still, it earns its length.

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

Completeness5/5

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

Given 4 parameters (2 required) and no output schema, the description covers purpose, usage, parameter details, reference system, output behavior, and examples. It is complete for an agent to select and invoke the tool correctly.

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

Parameters5/5

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

Schema coverage is 0%, so description must explain parameters. It provides clear descriptions for all four parameters, explains object reference syntax, and gives examples showing direct values, references, and mixed usage. This adds great value beyond the bare schema.

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 creates a new version of an existing pipeline with YAML configuration. It distinguishes from siblings like 'create_pipeline' (creates a new pipeline) and 'get_pipeline_version' (retrieves existing).

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

Usage Guidelines4/5

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

The description says 'Use this to update a pipeline's configuration', which is clear. But it doesn't explicitly contrast with 'patch_pipeline_version' or mention when to avoid using this tool. Still, examples provide good usage context.

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