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Federated MCP Architecture ​

What We Actually Built and Tested
Complete guide to integrating Research Library with Mnemoverse Docs through the standard MCP protocol

🎯 Overview ​

Imagine you have:

  • 📚 Research Library - specialized in searching and analyzing research data
  • 📖 Mnemoverse Docs - comprehensive technical documentation repository

Before: Research Library had no access to documentation
Now: Research Library can query documentation with "find everything about MCP" and get precise answers

In practice: When someone asks Research Library about a technical topic, it automatically checks current documentation and provides comprehensive responses.

✅ Current Implementation ​

1. Federated MCP Architecture 🔗 ​

Two independent services communicate through the standard MCP JSON-RPC 2.0 protocol:

┌─────────────────────┐    MCP JSON-RPC 2.0    ┌─────────────────────┐
│  Research Library   │ ────────────────────── │  Mnemoverse Docs    │
│  MCP Client         │    HTTP POST /mcp      │  MCP Server         │
│  (localhost:3001)   │                        │  (localhost:3003)   │
└─────────────────────┘                        └─────────────────────┘

2. Dual API Server for Documentation 🌐 ​

javascript
// mnemoverse-docs/api-server.js (port 3003)
app.post('/api/search', ...)     // HTTP REST API
app.post('/mcp', ...)            // MCP JSON-RPC endpoint
app.get('/.well-known/mcp', ...) // MCP Discovery

Capabilities:

  • ✅ HTTP REST API for standard web requests
  • ✅ MCP JSON-RPC for federated calls
  • ✅ Auto-discovery via /.well-known/mcp
  • ✅ CORS configuration for security

3. Ready-to-Use Federated MCP Client 📱 ​

python
# Simple to use
from mcp_servers.federated_mcp_client import FederatedMCPClient

client = FederatedMCPClient()
results = await client.search_docs("MCP protocol")
document = await client.get_document("about.md")

🧪 Test Results ​

bash
# Query: "MCP protocol"
✅ Found 3 documents in 0.09s
  1. MCP Server Development Guide (score: 4)
  2. Noosphere Layer Documentation (score: 1)
  3. API Integration Guide (score: 2)

Test 2: Document Retrieval ​

bash
# Document request: "about.md"
✅ Document retrieved in 0.01s
  📋 Title: about
  📊 Size: 9899 characters
  🕒 Modified: 2025-07-31T18:30:18.677Z

Test 3: Performance ​

bash
🏃‍♂️ 4 parallel requests:
⏱️  Sequential: 0.02s
🚀 Parallel: 0.01s
📈 Speedup: 1.4x

Test 4: Federated Integration ​

bash
✅ JSON-RPC 2.0 protocol working
✅ MCP discovery endpoint responding
✅ tools/list returning correct schemas
✅ tools/call executing search_docs and get_document
✅ Error handling working correctly

🚀 Real-World Use Cases ​

...existing code...

📁 Implementation Structure ​

Mnemoverse Docs (MCP Server) ​

mnemoverse-docs/
├── api-server.js                 # 🆕 Dual HTTP+MCP server
├── docs/                        # Existing documentation
└── package.json                 # API server dependencies

Research Library (MCP Client) ​

mnemoverse-research-library/
├── src/mcp_servers/
│   ├── research_library_server.py      # Main MCP server
│   └── federated_mcp_client.py         # 🆕 Federated client
├── test_comprehensive_federated_mcp.py  # 🆕 Comprehensive tests
└── demo_federated_research.py          # 🆕 Real scenario demos

🔧 Setup Instructions ​

1. Start mnemoverse-docs MCP Server ​

bash
cd /Users/eduardizgorodin/Projects/mnemoverse/mnemoverse-docs
node api-server.js &

# Check status
curl http://localhost:3003/health

2. Test Federated Calls ​

bash
cd /Users/eduardizgorodin/Projects/mnemoverse/mnemoverse-research-library
python test_comprehensive_federated_mcp.py

3. Run Real Scenario Demos ​

bash
python demo_federated_research.py

🛠️ Technical Implementation ​

...existing code...

⚡ Performance Metrics ​

...existing code...

❌ Pending Improvements ​

...existing code...

🚢 Deployment and CI/CD ​

...existing code...

🔍 Debugging and Diagnostics ​

...existing code...