Pipelines: validate, step_metadata, pipeline_run_notification, settings_demo, settings_source, templates_list, templates_get; and pipeline actions (operation=action). Create uses Micro-planner (tiny DAG): (1) Identify steps from user or registry, design DAG; suggest scan/security (code-scan, secu...
Risk signalsAccepts raw HTML/template content (payload) · High parameter count (13 properties)
Part of the Opsera server.
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AI agents invoke opsera_pipelines to trigger processes or run actions in Opsera. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
opsera_pipelines can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"opsera_pipelines": {
"limits": [
{
"counter": "opsera_pipelines_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Opsera policy for all 10 tools.
These attack patterns abuse exactly the kind of access opsera_pipelines gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Pipelines: validate, step_metadata, pipeline_run_notification, settings_demo, settings_source, templates_list, templates_get; and pipeline actions (operation=action). Create uses Micro-planner (tiny DAG): (1) Identify steps from user or registry, design DAG; suggest scan/security (code-scan, security-gate, compliance-sec, container-scan or Git Custodian/Grype if none); suggest approval gates for production deploys; display flow and GET USER CONFIRMATION. (2) Get step_metadata per step, fill fields, ALWAYS ask user when not derivable; use registry only when step metadata requires it. (3) Build JSON and call create pipeline action. Full workflow: docs/API_REFERENCE.md § opsera_pipelines.. It is categorised as a Execute tool in the Opsera MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Opsera MCP server in PolicyLayer and add a rule for opsera_pipelines: 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 Opsera. Nothing to install.
opsera_pipelines 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 opsera_pipelines 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 opsera_pipelines. 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.
opsera_pipelines is provided by the Opsera MCP server (https://agent.opsera.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 10 Opsera tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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