pipeline_run · Yaver MCP: Risk & Policy

WHAT IT DOES

What pipeline_run does on Yaver

AI agents invoke pipeline_run to trigger actions in Yaver. 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.

PARAMETERS

Parameter	Type	Required	Description
`job`	string	—	Specific job name (runs all if empty)
`file`	string	—	YAML file path (auto-detects from .github/workflows/ or .gitlab-ci.yml if empty)
`dry_run`	boolean	—	Print steps without executing

Parameters from the server's own tool schema.

WHY IT NEEDS A POLICY

Why pipeline_run needs a policy

pipeline_run triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.

Risk signalsAccepts file system path (file)

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

pipeline_run Rate-limited

pipeline_run 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": {
    "pipeline_run": {
      "limits": [
        {
          "counter": "pipeline_run_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Yaver, so you can run agents against it safely.

FAQ

Questions about pipeline_run

What does the pipeline_run tool do? +

Run a local CI/CD pipeline from GitHub Actions or GitLab CI YAML. Executes on the dev machine — no cloud runner needed. Hardware-aware, supports matrix builds, Docker services, caching, and artifacts. It is categorised as a Execute tool in the Yaver MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

What parameters does pipeline_run accept? +

pipeline_run accepts 3 parameters: job, file, dry_run. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on pipeline_run? +

Register the Yaver MCP server in PolicyLayer and add a rule for pipeline_run: 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 Yaver. Nothing to install.

What risk level is pipeline_run? +

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

Can I rate-limit pipeline_run? +

Yes. Add a rate_limit block to the pipeline_run 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 pipeline_run completely? +

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

pipeline_run is provided by the Yaver MCP server (yaver-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.