Save a plan to persistent storage for later retrieval and execution tracking.
AI agents use save_plan to create or update resources in Context Engine MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Context Engine MCP Server environment.
The tool creates or modifies a plan document in storage, which is a Write operation. It has low severity because saving a plan is non-destructive, does not execute code or external operations, and can be undone (plans can be deleted or overwritten).
From the tool's definition Tool description states 'Save a plan to persistent storage' — a create/modify operation that persists data reversibly.
Documented attack patterns abuse exactly the kind of access save_plan gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Context Engine MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for save_plan:
{
"version": "1",
"default": "deny",
"tools": {
"save_plan": {
"limits": [
{
"counter": "save_plan_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}save_plan stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Save a plan to persistent storage for later retrieval and execution tracking. It is categorised as a Write tool in the Context Engine MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Context Engine MCP Server MCP server in PolicyLayer and add a rule for save_plan: 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 Context Engine MCP Server. Nothing to install.
save_plan is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the save_plan 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 save_plan. 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.
save_plan is provided by the Context Engine MCP Server MCP server (kirachon/context-engine). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Context Engine MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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50 Context Engine MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.