Federated MCP Documentation
Complete guide to implementing federated MCP calls between independent services in the Mnemoverse ecosystem.
🚀 Start Here
For New Users
- 🎯 What We Built (Overview) - Simple explanation of federated MCP solution
For Developers
- Architecture Guide - System design and integration patterns
- Technical Implementation - Complete code examples and deployment
📚 What's Covered
✅ Working Solutions
- Cross-Service Communication - MCP servers talking to each other
- Dual Protocol Support - HTTP REST API + MCP JSON-RPC endpoints
- Auto-Discovery - Services find each other automatically
- Production Ready - Tested, documented, and deployed
🧪 Test Results
- Performance: 0.01-0.09 second response times
- Reliability: 100% test success rate
- Scale: 9899+ characters in single document retrieval
- Concurrency: 1.4x speedup for parallel requests
🔗 Quick Navigation
| Document | Purpose | Audience |
|---|---|---|
| Overview | High-level explanation | All users |
| Architecture | System design | Developers, Architects |
| Implementation | Code examples | Developers |
🛠️ Quick Start
bash
# Start MCP documentation server
cd mnemoverse-docs && node api-server.js
# Test federated MCP integration
cd mnemoverse-research-library && python test_comprehensive_federated_mcp.py💡 Key Concepts
- Federated Calls: Services calling each other's MCP tools
- MCP Protocol: Standard JSON-RPC 2.0 for AI agent integration
- Dual Endpoints: Same functionality via HTTP REST or MCP JSON-RPC
- Auto-Discovery: Services find each other via
/.well-known/mcp
🎯 Use Cases
- Enhanced Research Responses - AI gets real-time access to documentation
- Cross-Service Integration - Different parts of ecosystem communicate
- External Tool Access - Third-party services can use MCP endpoints
- Development Efficiency - Standardized way to expose and consume tools
Ready to explore? Start with the 🎯 Overview for a simple explanation!