CC-MCP
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🌟 Revolutionary AI Context Management: Solving LLM Memory Loss in Long Conversations
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- Intelligent context management for long conversations.
- Prevents “intent forgetting” in LLM agents.
- Maintains user goals, budgets, and timelines across turns.
- Eliminates context fragmentation between dialogue sessions.
- Provides persistent memory for repeated interactions.
- Works with any LLM that supports MCP.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
CC-MCPCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install CC-MCP and configure it with your MCP client (requires Node.js 18+). The server runs locally; details for specific commands or configuration keys are not provided in the README.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"cc-mcp": {
"cc-mcp": {
"autoApprove": [
"process_user_message",
"start_session",
"get_debug_info",
"list_sessions",
"get_session_stats",
"export_context",
"import_context",
"clear_context",
"end_session"
],
"disabled": false,
"timeout": 120,
"type": "stdio",
"command": "uv",
"args": [
"run",
"--directory",
"/path/to/your/cc-mcp",
"main.py"
],
"env": {
"CLASSIFIER_API_URL": "https://api.openai.com/v1/chat/completions",
"CLASSIFIER_API_KEY": "your_openai_api_key_here",
"CLASSIFIER_MODEL": "gpt-4o-mini"
}
}
}
}
}McpServers
{
"cc-mcp": {
"autoApprove": [
"process_user_message",
"start_session",
"get_debug_info",
"list_sessions",
"get_session_stats",
"export_context",
"import_context",
"clear_context",
"end_session"
],
"disabled": false,
"timeout": 120,
"type": "stdio",
"command": "uv",
"args": [
"run",
"--directory",
"/path/to/your/cc-mcp",
"main.py"
],
"env": {
"CLASSIFIER_API_URL": "https://api.openai.com/v1/chat/completions",
"CLASSIFIER_API_KEY": "your_openai_api_key_here",
"CLASSIFIER_MODEL": "gpt-4o-mini"
}
}
}🌟 Revolutionary AI Context Management: Solving LLM Memory Loss in Long Conversations
Long-term conversational consistency management system for LLM-powered AI agents
🚀 The Problem We Solve
Traditional LLMs suffer from "intent forgetting" in long conversations:
❌ Lose track of the original goal after a few turns
❌ Forget important constraints and decisions
❌ Provide inconsistent responses across dialogue sessions
❌ Require users to constantly remind the AI of context
CC-MCP provides intelligent context management tools to help MCP clients maintain consistency.
🎯 Real-World Impact
Before CC-MCP:
User: "I want to build an AI assistant app"
AI: "Sure! Here are some general approaches..."
[10 messages later]
User: "Remember, budget is 500K yen, 3 months timeline"
AI: "What project are we talking about?" ❌
After CC-MCP:
User: "I want to build an AI assistant app"
AI: "Great! Let me help you design this system..."
[10 messages later]
User: "What about deployment options?"
AI: "For your AI assistant app (budget: 500K yen, 3-month timeline, security priority), here are deployment strategies that fit your constraints..." ✅
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