User Feedback
MCP ServerFree** - Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor.
Capabilities5 decomposed
human-in-the-loop feedback collection via mcp protocol
Medium confidenceImplements a Model Context Protocol (MCP) server that exposes a standardized interface for AI agents (Cline, Cursor) to pause execution and request human feedback before proceeding. The server acts as a bridge between the agent's decision-making loop and the human operator, using MCP's tool-calling mechanism to invoke feedback requests that block agent execution until a human response is received.
Provides a lightweight MCP server specifically designed for human-in-the-loop workflows in AI code editors (Cline, Cursor), using MCP's native tool-calling protocol rather than custom HTTP endpoints or polling mechanisms, enabling seamless integration with existing agent architectures.
Simpler and more integrated than building custom HTTP endpoints or webhook systems — leverages MCP's standardized tool-calling interface that Cline and Cursor already understand natively.
blocking feedback request with agent execution pause
Medium confidenceExposes a tool that agents can invoke to request human feedback, which synchronously blocks the agent's execution loop until the human provides a response. The MCP server queues the feedback request, displays it to the human operator (via stdout, IDE UI, or connected interface), waits for input, and returns the human's decision back to the agent to resume execution.
Implements synchronous blocking feedback as an MCP tool rather than an async callback or event system, ensuring agent execution halts until human input is received — a critical safety pattern for code-generation agents where asynchronous feedback could lead to race conditions.
More reliable than async feedback systems because it guarantees the agent cannot proceed until human approval is explicit, whereas webhook-based approaches risk the agent continuing if the callback is delayed or lost.
mcp tool registration and schema exposure
Medium confidenceRegisters feedback-related tools with the MCP protocol's tool registry, exposing their schemas (name, description, parameters) to the connected client so the agent can discover and invoke them. The server implements MCP's tool-definition interface, allowing clients like Cline to understand what feedback tools are available and how to call them with proper parameter validation.
Implements MCP's tool-definition interface to expose feedback tools as discoverable, schema-validated capabilities rather than hardcoded endpoints, enabling clients to understand tool contracts before invocation.
More discoverable and self-documenting than REST endpoints because tool schemas are machine-readable and clients can validate parameters before sending requests, reducing runtime errors.
agent-to-human communication bridge via mcp
Medium confidenceActs as a communication intermediary between the AI agent and the human operator, translating agent feedback requests into human-readable prompts and returning human responses back to the agent in a format the agent can process. The server manages the bidirectional message flow, ensuring context is preserved and responses are properly formatted for agent consumption.
Provides a lightweight message-passing bridge specifically for agent-human communication over MCP, avoiding the complexity of full conversation management systems while maintaining bidirectional context flow.
Simpler than building a full chat interface or conversation management system because it leverages MCP's existing tool-calling mechanism for request/response patterns rather than implementing custom messaging protocols.
integration with cline and cursor agent environments
Medium confidenceProvides native integration with Cline and Cursor's agent execution environments by implementing the MCP protocol that these tools natively support. The server can be registered as an MCP server in these IDEs' configuration, allowing agents running in Cline/Cursor to automatically discover and invoke feedback tools without custom client code.
Provides drop-in MCP server integration for Cline and Cursor without requiring modifications to agent code or IDE plugins, leveraging these tools' native MCP support to add human-in-the-loop capabilities.
Easier to deploy than custom Cline/Cursor plugins because it uses the standard MCP protocol these IDEs already support, avoiding the need to build and maintain IDE-specific extensions.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓developers building autonomous agents in Cline or Cursor who need safety guardrails
- ✓teams implementing human-in-the-loop AI workflows where agent autonomy must be bounded
- ✓solo developers prototyping AI agents who want interactive debugging and validation
- ✓developers implementing safety checkpoints in autonomous code agents
- ✓teams building interactive AI workflows where human oversight is mandatory at certain steps
- ✓prototyping scenarios where agent behavior needs real-time human validation
- ✓MCP server developers implementing custom tools for agents
- ✓teams building extensible agent frameworks where tool discovery is dynamic
Known Limitations
- ⚠Blocks agent execution synchronously — no timeout mechanism means hung agents if human never responds
- ⚠No built-in persistence of feedback history or audit trail across sessions
- ⚠Single-threaded feedback collection — cannot handle concurrent feedback requests from multiple agent instances
- ⚠Requires manual integration into agent prompt/system design — no automatic detection of decision points
- ⚠Synchronous blocking design means agent cannot perform other work while waiting for feedback
- ⚠No timeout or fallback mechanism — if human never responds, agent hangs indefinitely
Requirements
Input / Output
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** - Simple MCP Server to enable a human-in-the-loop workflow in tools like Cline and Cursor.
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