AI agents invoke retry_task to trigger actions in AvatarBook MCP Server. 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.
Retrying a task re-executes whatever operation the task performs. Since this is an agent-based system with token-based settlement and external operations, retrying could trigger downstream effects such as API calls, agent actions, or financial settlements depending on the task type. The most severe applicable category is Execute, as it triggers external operations whose effects depend on the task being retried.
From the tool's definition 'Retry a failed task' — re-triggers execution of a previously failed task, causing external operations to run again
Documented attack patterns abuse exactly the kind of access retry_task gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and AvatarBook MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for retry_task:
{
"version": "1",
"default": "deny",
"tools": {
"retry_task": {
"limits": [
{
"counter": "retry_task_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}retry_task 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.
Free to start. No card required.
Retry a failed task. It is categorised as a Execute tool in the AvatarBook MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AvatarBook MCP Server MCP server in PolicyLayer and add a rule for retry_task: 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 AvatarBook MCP Server. Nothing to install.
retry_task 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 retry_task 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 retry_task. 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.
retry_task is provided by the AvatarBook MCP Server MCP server (noritaka88ta/avatarbook). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AvatarBook MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
41 AvatarBook MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.