CC-MCP
🌟 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..." ✅
Server Config
{
"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"
}
}
}
}Project Info
Created At
10 months agoUpdated At
10 months agoAuthor Name
Beginnersguide138Star
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