This record as markdown: /tools/dkeeno-gitlab-mcp-server/gitlab-retry-pipeline.md
What gitlab_retry_pipeline does on Gitlab
AI agents invoke gitlab_retry_pipeline to trigger actions in Gitlab. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why gitlab_retry_pipeline is rated High
Retrying a pipeline re-executes CI/CD jobs, triggering external operations such as builds, tests, or deployments. This is an Execute-category action as it runs automated processes whose effects depend on the pipeline configuration. It is not Destructive since it doesn't delete data, and not Write since it doesn't create/modify data directly.
From the tool's definition Retry failed jobs in a pipeline
Attacks that exploit this kind of access
The rule that runs gitlab_retry_pipeline safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gitlab, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For gitlab_retry_pipeline, this is the rule to start with:
gitlab_retry_pipeline stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Gitlab, apply this rule, and every gitlab_retry_pipeline call is checked against it from then on.
Questions about gitlab_retry_pipeline
Retry failed jobs in a pipeline. It is categorised as a Execute tool in the Gitlab MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Gitlab MCP server in PolicyLayer and add a rule for gitlab_retry_pipeline: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Gitlab. Nothing to install.
gitlab_retry_pipeline is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the gitlab_retry_pipeline rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for gitlab_retry_pipeline. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
gitlab_retry_pipeline is provided by the Gitlab MCP server (dkeeno/gitlab-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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