Get user feedback for a flow. Use this at the start of a session to check if the user has provided feedback on: - The implementation plan (approval phase) - The code implementation (review phase, when rejected back to in_progress) If feedback exists, you should address it before continuing work.
AI agents call flow_get_feedback to retrieve information from DevFlow MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves feedback information from the DevFlow system without creating, modifying, deleting, or executing any operations. It has no side effects beyond data retrieval, making it a Read category tool. The severity is low because retrieving feedback data poses minimal risk even if misused by an AI agent.
From the tool's definition Tool name 'flow_get_feedback' and description states 'Get user feedback' - retrieves existing feedback data with no modification, creation, or side effects.
Attacks that exploit this kind of access
Get user feedback for a flow. Use this at the start of a session to check if the user has provided feedback on: - The implementation plan (approval phase) - The code implementation (review phase, when rejected back to in_progress) If feedback exists, you should address it before continuing work. It is categorised as a Read tool in the DevFlow MCP Server MCP Server, which means it retrieves data without modifying state.
Register the DevFlow MCP Server MCP server in PolicyLayer and add a rule for flow_get_feedback: 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 DevFlow MCP Server. Nothing to install.
flow_get_feedback is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the flow_get_feedback 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 flow_get_feedback. 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.
flow_get_feedback is provided by the DevFlow MCP Server MCP server (klausfreiberufler/devflow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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