play_pipeline_schedule
Run a pipeline schedule immediately
This record as markdown: /tools/io-github-zereight-gitlab-mcp/play-pipeline-schedule.md
What play_pipeline_schedule does on Gitlab Mcp
AI agents invoke play_pipeline_schedule to trigger actions in Gitlab Mcp. 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 play_pipeline_schedule is rated High
Triggers a pipeline execution immediately, running arbitrary CI/CD jobs on the server.
From the tool's definition Run a pipeline schedule immediately
Attacks that exploit this kind of access
The rule that runs play_pipeline_schedule safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gitlab Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For play_pipeline_schedule, this is the rule to start with:
play_pipeline_schedule 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 Mcp, apply this rule, and every play_pipeline_schedule call is checked against it from then on.
Questions about play_pipeline_schedule
Run a pipeline schedule immediately. It is categorised as a Execute tool in the Gitlab Mcp 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 play_pipeline_schedule: 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 Mcp. Nothing to install.
play_pipeline_schedule 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 play_pipeline_schedule 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 play_pipeline_schedule. 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.
play_pipeline_schedule is provided by the Gitlab MCP server (@zereight/mcp-gitlab). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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