play_pipeline_jobs

Play multiple manual pipeline jobs sequentially

SERVERGitlab Mcp SOURCE@zereight/mcp-gitlab

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-zereight-gitlab-mcp/play-pipeline-jobs.md

WHAT IT DOES

What play_pipeline_jobs does on Gitlab Mcp

AI agents invoke play_pipeline_jobs 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.

RISK

Why play_pipeline_jobs is rated High

Triggers execution of CI/CD pipeline jobs, causing external operations with potentially broad effects.

From the tool's definition Play multiple manual pipeline jobs sequentially

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs play_pipeline_jobs 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_jobs, this is the rule to start with:

play_pipeline_jobs Rate-limited

play_pipeline_jobs 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "play_pipeline_jobs": {
      "limits": [
        {
          "counter": "play_pipeline_jobs_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Gitlab Mcp, apply this rule, and every play_pipeline_jobs call is checked against it from then on.

FAQ

Questions about play_pipeline_jobs

What does the play_pipeline_jobs tool do? +

Play multiple manual pipeline jobs sequentially. 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.

How do I enforce a policy on play_pipeline_jobs? +

Register the Gitlab MCP server in PolicyLayer and add a rule for play_pipeline_jobs: 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.

What risk level is play_pipeline_jobs? +

play_pipeline_jobs is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit play_pipeline_jobs? +

Yes. Add a rate_limit block to the play_pipeline_jobs 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.

How do I block play_pipeline_jobs completely? +

Set action: deny in the PolicyLayer policy for play_pipeline_jobs. 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.

What MCP server provides play_pipeline_jobs? +

play_pipeline_jobs 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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