Refine an existing implementation plan based on feedback or clarifications. Use this tool to iterate on a plan after reviewing it or answering clarifying questions. When to use this tool: - After reviewing a plan and wanting adjustments - To answer questions the plan raised - To add more detail t...
AI agents use refine_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.
This tool creates or modifies data (an implementation plan) in a reversible manner. It does not execute code, delete data, or move money. The modification is iterative and can be undone by creating a new plan or reverting to a previous version.
From the tool's definition Tool description states "Refine an existing implementation plan" and "iterate on a plan"; inputs include "feedback or clarifications" and "specific steps to focus on"; this modifies an existing plan document by updating its content based on provided feedback.
Documented attack patterns abuse exactly the kind of access refine_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 refine_plan:
{
"version": "1",
"default": "deny",
"tools": {
"refine_plan": {
"limits": [
{
"counter": "refine_plan_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}refine_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.
Free to start. No card required.
Refine an existing implementation plan based on feedback or clarifications. Use this tool to iterate on a plan after reviewing it or answering clarifying questions. When to use this tool: - After reviewing a plan and wanting adjustments - To answer questions the plan raised - To add more detail to specific steps - To change the approach based on new information Input: - The current plan (JSON from a previous create_plan call) - Your feedback or clarifications - Optionally, specific steps to focus on. 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 refine_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.
refine_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 refine_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 refine_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.
refine_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.
Free to start. No card required.
50 Context Engine MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.