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ZenML MCP Server

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by zenml-io

trigger_pipeline

Execute a ZenML pipeline run by specifying the pipeline name or ID, with options to select a specific snapshot or override the stack configuration.

Instructions

Trigger a pipeline to run from the server.

Args:
    pipeline_name_or_id: The name or ID of the pipeline to trigger
    snapshot_name_or_id: The name or ID of a specific snapshot to run (preferred)
    stack_name_or_id: Optional stack override for the run
    template_id: ⚠️ DEPRECATED - Use `snapshot_name_or_id` instead.
        The ID of a run template to use. Run Templates are deprecated
        and will be removed in a future version.

Usage examples:
    * Run the latest runnable snapshot for a pipeline:
    ```python
    trigger_pipeline(pipeline_name_or_id=<NAME>)
    ```
    * Run the latest runnable snapshot for a pipeline on a specific stack:
    ```python
    trigger_pipeline(
        pipeline_name_or_id=<NAME>,
        stack_name_or_id=<STACK_NAME_OR_ID>
    )
    ```
    * Run a specific snapshot (RECOMMENDED):
    ```python
    trigger_pipeline(
        pipeline_name_or_id=<NAME>,
        snapshot_name_or_id=<SNAPSHOT_NAME_OR_ID>
    )
    ```
    * Run a specific template (DEPRECATED - use snapshot_name_or_id instead):
    ```python
    trigger_pipeline(pipeline_name_or_id=<NAME>, template_id=<ID>)
    ```

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pipeline_name_or_idYes
snapshot_name_or_idNo
stack_name_or_idNo
template_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by indicating this triggers an execution ('run from the server'), implying it's a write/mutation operation. It provides important behavioral context about deprecation (template_id) and preferred parameters (snapshot_name_or_id). However, it doesn't mention potential side effects like resource consumption, permissions needed, or what happens if a pipeline is already running.

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

Conciseness5/5

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

The description is efficiently structured with clear sections: purpose statement, args explanation with semantic details, and comprehensive usage examples. Every sentence adds value - the deprecation warning is necessary, and the examples demonstrate different scenarios without redundancy. The information is front-loaded with the core purpose first.

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?

For a 4-parameter mutation tool with no annotations but with an output schema, the description provides excellent parameter semantics and usage guidance. It covers the essential 'what' and 'how' but could benefit from mentioning behavioral aspects like error conditions, permissions, or what the output contains. The existence of an output schema reduces the need to describe return values, but some operational context is still missing.

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?

With 0% schema description coverage, the description fully compensates by explaining all 4 parameters in detail. It clarifies that pipeline_name_or_id is required, explains the purpose of each optional parameter, provides deprecation context for template_id, and distinguishes between name vs. ID usage. The usage examples demonstrate practical parameter combinations.

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 specific action ('Trigger a pipeline to run from the server') with the resource ('pipeline'), distinguishing it from sibling tools like get_pipeline_details or list_pipelines which are read-only operations. The verb 'trigger' is precise and indicates an execution action rather than retrieval.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use different parameter combinations through detailed usage examples, including a recommended approach ('Run a specific snapshot (RECOMMENDED)') and clear deprecation warnings ('⚠️ DEPRECATED - Use `snapshot_name_or_id` instead'). It distinguishes between running latest vs. specific snapshots and when to use stack overrides.

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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