Are you reaching your Cursor Premium 500 fast request limit much sooner than expected? If you're an API developer, backend engineer, or part of a product-focused team, you know how frustrating it is to have your coding workflow interrupted by "You've hit your limit of 500 fast requests." What if you could effectively double your request efficiency—making those 500 requests feel like 1,000 without upgrading your plan?
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Why Cursor Premium Users Hit API Limits So Fast
Cursor Premium is popular for its AI-powered coding assistant, but many developers burn through their 500 monthly fast requests in just 10–15 days. Why?
- Strict monthly limits (500 fast requests for most plans)
- No transparent reset schedule
- Request queuing and delays once limits are exceeded
- Coding flow disruptions during critical moments
For professional developers or teams, this can make Cursor feel more suited for hobbyists than for production-grade work.
The Solution: Interactive Feedback MCP Server
What if every API call counted for more? The Interactive Feedback MCP Server is a powerful tool that sits between you and Cursor’s AI, ensuring every request is more intentional, accurate, and efficient.
How Does It Work?
The server uses the Model Context Protocol (MCP) to introduce a "human-in-the-loop" process:
- The AI asks for your confirmation before major actions or code changes.
- It requests targeted feedback when handling complex tasks.
- It clarifies its understanding of your prompts before proceeding.
- Each step is optimized for accuracy, preventing wasted requests on incorrect or undesired outputs.
Result: Your 500 requests can feel like 1,000—because fewer are wasted and every interaction is more productive.
Step-by-Step: Set Up MCP Feedback Enhanced with Cursor
This guide uses the mcp-feedback-enhanced server (Minidoracat/mcp-feedback-enhanced fork), which supports both GUI and web interfaces.
Prerequisites
Before you begin, ensure you have:
- An active Cursor Premium subscription
uv(a Python packaging tool). Install via:pip install uv- Git (for developer installation)
- Command-line familiarity (Terminal, Command Prompt, or PowerShell)
- Admin permissions if required
Step 1: Install and Test the MCP Server
The easiest way to test is with uvx:
uvx mcp-feedback-enhanced@latest test
This command checks if the server runs correctly on your machine. It auto-selects the right interface (Qt GUI or Web UI) based on your environment.
For advanced/dev installation:
git clone https://github.com/Minidoracat/mcp-feedback-enhanced.git
cd mcp-feedback-enhanced
uv sync
Step 2: Run the MCP Feedback Enhanced Server
If you did a developer install, make sure you’re in the mcp-feedback-enhanced directory.
For standalone interface testing:
- Qt GUI (local):
uvx mcp-feedback-enhanced@latest test --gui - Web UI (remote/WSL):
uvx mcp-feedback-enhanced@latest test --web
Normally, the server is run automatically by Cursor’s MCP configuration (next step).
Step 3: Configure Cursor to Use the MCP Server
Open Cursor, then:
- Press
Cmd + Shift + P(macOS) orCtrl + Shift + P(Windows/Linux) to open the command palette. - Type "Cursor Settings" and open it.
- In the "MCP" section, add or modify your server configuration. Example:
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}
For advanced options (force web UI, debug):
{
"mcpServers": {
"mcp-feedback-enhanced": {
"command": "uvx",
"args": ["mcp-feedback-enhanced@latest"],
"timeout": 600,
"env": {
"FORCE_WEB": "true",
"MCP_DEBUG": "false"
},
"autoApprove": ["interactive_feedback"]
}
}
}
Paste the JSON into the appropriate settings area in Cursor.
Step 4: Set Custom Prompts for AI Feedback
For best results, update your assistant’s custom prompts to enforce interactive feedback. In Cursor’s "Prompts" or "Custom Prompts" settings, add:
# MCP Interactive Feedback Rules
1. During any process, task, or conversation, call MCP mcp-feedback-enhanced.
2. On receiving user feedback, call MCP mcp-feedback-enhanced again, adjusting as needed.
3. Only stop calling MCP on explicit "end" or "no more interaction" instructions.
4. Before task completion, use MCP to request user feedback.
(Tailor the wording to ensure confirmation at key stages—this reduces wasted requests.)
Step 5: Test and Optimize Your Setup
- Save your MCP configuration in Cursor.
- Restart Cursor to apply changes.
- Start a new chat or code session; assign a task to the AI.
- The AI should now check in with you at important steps, using mcp-feedback-enhanced.
Benefits:
- Fewer Misinterpretations: The AI confirms before acting, preventing wasted requests.
- Better Output Quality: Iterative feedback refines results.
- Streamlined Workflow: Direct feedback reduces the need for multiple clarifications.
- Optimized Usage: Productive interactions stretch your quota further.
Why API Teams Should Care
If you rely on AI tools like Cursor for development speed, every wasted request is lost productivity. By implementing mcp-feedback-enhanced, you gain control over your AI assistant—making it smarter and more responsive to your feedback.
For API-driven teams, streamlining workflows is key. Apidog, for example, is designed with similar developer-centric philosophy: focusing on efficiency, collaboration, and clear documentation. Tools that maximize every interaction—whether for API testing or AI coding—are essential for modern engineering teams.
Conclusion: Get More Value from Your Cursor Premium Subscription
Don’t let API request limits disrupt your coding. The mcp-feedback-enhanced server introduces a practical "human-in-the-loop" system, making every AI interaction count. With this setup, you waste fewer requests, get higher quality results, and extend the value of your Cursor Premium plan—keeping you in the flow.
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