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Run pipeline

gitlab_run_pipeline

Triggers a GitLab CI/CD pipeline for a specified project and branch or ref, returning pipeline details. Use it to start builds, tests, or deployments from automation.

Instructions

Run a pipeline for a specific project.

Args: **kwargs: Additional parameters for the request (e.g., project_id, ref).

Returns: Response: A wrapper containing the original response and a Pipeline model.

Raises: MissingParameterError: If the project_id or ref is missing. ParameterError: If invalid parameters are provided.

Input Schema

NameRequiredDescriptionDefault
refNo
dataNo
pageNo
statusNo
per_pageNo
max_pagesNo
variablesNo
project_idNo
pipeline_idNo
total_pagesNo
api_parametersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv27.3.13

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already declare a non-read-only, non-idempotent, open-world mutation, so the safety profile is covered. The description adds value by naming the failure modes (MissingParameterError for missing project_id/ref, ParameterError for invalid params) and the Pipeline model return wrapper, but says nothing about side effects, permissions, or what happens to existing pipelines.

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

Conciseness3/5

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

The core sentence is front-loaded and efficient, but the 'Args: **kwargs' docstring boilerplate is generic and does not match the actual 11-parameter schema, so it adds noise rather than structure.

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?

For a mutation tool with 11 undocumented parameters and no output schema, the definition should explain required inputs, the meaning of ref/variables, and the side effects of triggering a pipeline. It covers only the return wrapper and error cases, leaving the agent under-equipped to call it correctly.

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

Parameters2/5

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

Schema coverage is 0% across 11 parameters, so the description carries full burden — yet it only name-drops project_id and ref as examples and never explains what ref means (branch/tag), what variables/data/api_parameters do, or why pagination params exist on a run operation. This leaves most parameters undocumented anywhere.

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

Purpose4/5

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

States a specific verb and resource ('Run a pipeline') scoped to a project, which is clear enough to act on. It does not distinguish itself from close siblings such as gitlab_graphql_ops_create_pipeline or gitlab_run_pipeline_schedule, so it stops short of a 5.

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?

There is no guidance on when to use this tool versus the GraphQL create_pipeline variant or run_pipeline_schedule, nor on prerequisites like permissions or branch state. The only conditional content is about error raising, not usage selection.

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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