The most comprehensive Redis MCP Server for Alphe.AI - Optimized for sub-5 second response times with multi-layer caching
npm install alphe-redis-mcp-serverThe most comprehensive Redis MCP Server optimized for sub-5 second response times with cognitive enhancement
``bash`
npm install @alphe-ai/redis-mcp-server
Create .env file:
`bashZilliz Configuration (Your cluster)
ZILLIZ_CLUSTER_ID=in05-2ea3b0b61c1812b
ZILLIZ_ENDPOINT=https://in05-2ea3b0b61c1812b.serverless.aws-eu-central-1.cloud.zilliz.com
ZILLIZ_TOKEN=your_token_here
ZILLIZ_USERNAME=db_2ea3b0b61c1812b
ZILLIZ_PASSWORD=your_password_here
$3
Add to
claude_desktop_config.json:`json
{
"mcpServers": {
"alphe-redis": {
"command": "npx",
"args": [
"@alphe-ai/redis-mcp-server"
],
"env": {
"ZILLIZ_CLUSTER_ID": "in05-2ea3b0b61c1812b",
"ZILLIZ_ENDPOINT": "https://in05-2ea3b0b61c1812b.serverless.aws-eu-central-1.cloud.zilliz.com",
"ZILLIZ_TOKEN": "your_token",
"ZILLIZ_USERNAME": "db_2ea3b0b61c1812b",
"ZILLIZ_PASSWORD": "your_password"
}
}
}
}
`๐ฎ Usage Examples
$3
`bash
Process query through cognitive pipeline
redis_tool_call cognitive_query {
"query": "Explain how Redis clustering works",
"context": {"domain": "tech", "urgency": 8},
"useCache": true
}
`$3
`bash
Set with intelligent caching
redis_tool_call redis_set {
"key": "user:1234",
"value": "John Doe",
"options": {
"ex": 3600,
"compress": true,
"priority": 8,
"namespace": "users"
}
}Get with automatic fallback
redis_tool_call redis_get {
"key": "user:1234",
"useCache": true
}
`$3
`bash
redis_tool_call semantic_search {
"query": "machine learning algorithms",
"limit": 10,
"minSimilarity": 0.8
}
`$3
`bash
redis_tool_call get_performance_metrics {
"includeAgents": true
}
`๐ Performance Benchmarks
| Operation | Traditional Redis | Alphe Redis MCP |
|-----------|------------------|------------------|
| Simple GET | ~2ms | < 1ms (L1 cache) |
| Complex Query | ~500ms | < 100ms (cognitive) |
| Vector Search | ~2s | < 200ms (cached) |
| Batch Operations | ~50ms | < 10ms (optimized) |
๐ง Architecture
`
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ COGNITIVE LAYER โ
โ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ
โ โPerceptโ โContextโ โReason โ โOrchestrโ
โ โion โ โEngine โ โing โ โator โ
โ โAgent โ โer โ โAgent โ โAgent โ
โ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ โโโโโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ MULTI-LAYER CACHE โ
โ L1: Memory โ L2: Redis โ L3: Upstash โ
โ L4: Zilliz โ L5: Supabase โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ MCP INTERFACE โ
โ โข Tool Calls โข Resources โข Streaming โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
`๐ญ Cognitive Agents Status
Monitor your agents in real-time:
`bash
redis_tool_call get_performance_metrics {
"includeAgents": true
}
`Expected Output:
`json
{
"cognitive": {
"agents": {
"perception_agent": {
"model": "gemma2-9b",
"status": "busy",
"avgLatency": 150,
"queueLength": 0
},
"context_engineer": {
"model": "phi-3-mini",
"status": "idle",
"avgLatency": 100,
"queueLength": 0
}
}
}
}
`๐จ Troubleshooting
$3
1. Check Ollama is running: ollama serve
2. Verify models are installed:
`bash
ollama pull gemma2:9b
ollama pull phi3:mini
ollama pull qwq:32b
ollama pull deepseek-r1
ollama pull llama3.3:70b
ollama pull mixtral:8x7b
`
3. Test agent connectivity: Each agent should respond to health checks$3
1. Check cache hit rates in performance metrics
2. Monitor memory usage - increase MAX_CACHE_SIZE_MB
3. Enable compression for large values
4. Use batch operations for multiple requests$3
1. Verify Redis connection: redis-cli ping
2. Check Zilliz cluster status in Zilliz Cloud console
3. Test Supabase connection with provided credentials๐ API Reference
$3
-
redis_set - Set string value with multi-layer caching
- redis_get - Get value with intelligent fallback
- redis_mget/mset - Batch operations with optimization
- cognitive_query - Process through cognitive pipeline
- semantic_search - Vector-based similarity search
- get_performance_metrics - System performance stats$3
-
redis://health - System health status
- redis://performance - Performance metrics
- redis://cognitive-status - Cognitive agents status
- redis://cache-stats - Cache layer statistics๐ Security
- Environment variable based configuration
- No hardcoded credentials
- Secure connections to all services
- Optional authentication for all layers
๐ค Contributing
1. Fork the repository
2. Create feature branch (
git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature`)MIT ยฉ Alphe.AI
- ๐ง Email: support@alphe.ai
- ๐ฌ Discord: Alphe.AI Community
- ๐ Docs: docs.alphe.ai
- ๐ Issues: GitHub Issues
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Built with โค๏ธ by the Alphe.AI team for the Claude Code community