play_pipeline_job

Run a manual pipeline job

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-job.md

WHAT IT DOES

What play_pipeline_job does on Gitlab Mcp

AI agents invoke play_pipeline_job 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_job is rated High

This tool runs/executes a manual pipeline job, which means it triggers code execution in a CI/CD environment. The effects depend entirely on what that job is configured to do (build, test, deploy, etc.), making it an Execute category risk.

From the tool's definition Tool name 'play_pipeline_job' with description 'Run a manual pipeline job' indicates execution of a pipeline job, which triggers external operations and code execution in GitLab CI/CD infrastructure.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs play_pipeline_job 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_job, this is the rule to start with:

play_pipeline_job Rate-limited

play_pipeline_job 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_job": {
      "limits": [
        {
          "counter": "play_pipeline_job_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_job call is checked against it from then on.

FAQ

Questions about play_pipeline_job

What does the play_pipeline_job tool do? +

Run a manual pipeline job. 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_job? +

Register the Gitlab MCP server in PolicyLayer and add a rule for play_pipeline_job: 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_job? +

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

Can I rate-limit play_pipeline_job? +

Yes. Add a rate_limit block to the play_pipeline_job 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_job completely? +

Set action: deny in the PolicyLayer policy for play_pipeline_job. 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_job? +

play_pipeline_job 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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