replay_workflow_run
Replay or plan replay for a workflow run. Defaults to dry-run; live replay remains policy/audit gated.
This record as markdown: /tools/adrata-adrata-mcp/replay-workflow-run.md
What replay_workflow_run does on Adrata
AI agents invoke replay_workflow_run to trigger actions in Adrata. 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.
Why replay_workflow_run is rated High
replay_workflow_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.
Attacks that exploit this kind of access
The rule that runs replay_workflow_run safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Adrata, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For replay_workflow_run, this is the rule to start with:
replay_workflow_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.
The button opens the PolicyLayer dashboard: create your workspace, connect Adrata, apply this rule, and every replay_workflow_run call is checked against it from then on.
Questions about replay_workflow_run
Replay or plan replay for a workflow run. Defaults to dry-run; live replay remains policy/audit gated. It is categorised as a Execute tool in the Adrata MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Adrata MCP server in PolicyLayer and add a rule for replay_workflow_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 Adrata. Nothing to install.
replay_workflow_run 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 replay_workflow_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.
Set action: deny in the PolicyLayer policy for replay_workflow_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.
replay_workflow_run is provided by the Adrata MCP server (@adrata/adrata-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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