retry_pipeline_job_run

[Pipeline Management] Retry a failed pipeline job run.

SERVERAlibabacloud Devops SOURCEalibabacloud-devops-mcp-server

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/alibabacloud-devops/retry-pipeline-job-run.md

WHAT IT DOES

What retry_pipeline_job_run does on Alibabacloud Devops

AI agents invoke retry_pipeline_job_run to trigger actions in Alibabacloud Devops. 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 retry_pipeline_job_run is rated High

Retrying a pipeline job run causes external systems (build servers, deployment platforms) to execute code and perform actions. While not destructive or financial in isolation, a failed pipeline job might have partially completed side effects (partial deployments, partial state changes), and retrying could propagate those effects or trigger new operations in development/staging/production environments.

From the tool's definition Tool description states 'Retry a failed pipeline job run' — this triggers execution of a pipeline job, which re-runs code/CI-CD actions whose effects depend on the job's configuration and target environment.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs retry_pipeline_job_run safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Alibabacloud Devops, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For retry_pipeline_job_run, this is the rule to start with:

retry_pipeline_job_run Rate-limited

retry_pipeline_job_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": {
    "retry_pipeline_job_run": {
      "limits": [
        {
          "counter": "retry_pipeline_job_run_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 Alibabacloud Devops, apply this rule, and every retry_pipeline_job_run call is checked against it from then on.

FAQ

Questions about retry_pipeline_job_run

What does the retry_pipeline_job_run tool do? +

[Pipeline Management] Retry a failed pipeline job run. It is categorised as a Execute tool in the Alibabacloud Devops MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on retry_pipeline_job_run? +

Register the Alibabacloud Devops MCP server in PolicyLayer and add a rule for retry_pipeline_job_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 Alibabacloud Devops. Nothing to install.

What risk level is retry_pipeline_job_run? +

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

Can I rate-limit retry_pipeline_job_run? +

Yes. Add a rate_limit block to the retry_pipeline_job_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 retry_pipeline_job_run completely? +

Set action: deny in the PolicyLayer policy for retry_pipeline_job_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 retry_pipeline_job_run? +

retry_pipeline_job_run is provided by the Alibabacloud Devops MCP server (alibabacloud-devops-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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