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MCP Aggregator Server

by NguyenTrinh3008
GitHub
Python
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MCP Aggregator Server

Unified MCP interface that proxies requests to multiple backend MCP servers.

Architecture

┌─────────────────────────────────────────────────────────────┐ │ MCP Client │ │ (Claude, IDE, etc.) │ └────────────────────┬────────────────────────────────────────┘ │ │ Connect to single endpoint ▼ ┌─────────────────────────────────────────────────────────────┐ │ Aggregator MCP Server (Port 8003) │ │ ┌──────────────────────────────────────────────────────┐ │ │ │ Unified MCP Interface │ │ │ │ - 19 tools total (2 health + 10 memory + 7 vector) │ │ │ │ - Handles routing internally │ │ │ │ - Single /mcp/sse & /mcp/messages endpoint │ │ │ └──────────────────────────────────────────────────────┘ │ └────────┬──────────────────────────────────────────────────┬──┘ │ │ │ HTTP Proxy │ HTTP Proxy ▼ ▼ ┌──────────────────────┐ ┌──────────────────────┐ │ ZepAI Memory Server │ │ LTM Vector Server │ │ (Port 8002) │ │ (Port 8000) │ │ │ │ │ │ - Knowledge Graph │ │ - Vector Database │ │ - Conversation Memory│ │ - Code Indexing │ │ - 10 tools │ │ - 7 tools │ └──────────────────────┘ └──────────────────────┘

Features

  • Unified Interface: Single MCP endpoint for all connected servers

  • Transparent Proxying: Automatically routes requests to appropriate backend servers

  • Health Monitoring: Built-in health checks for all connected servers

  • Retry Logic: Automatic retry with exponential backoff for failed requests

  • Error Handling: Comprehensive error handling and logging

  • Extensible: Easy to add new backend servers

Installation

  1. Install dependencies:

pip install -r requirements.txt

  1. Configure environment (edit .env):

# Aggregator Server AGGREGATOR_HOST=0.0.0.0 AGGREGATOR_PORT=8003 # Memory Server (FastMCP Server) MEMORY_SERVER_URL=http://localhost:8002 MEMORY_SERVER_TIMEOUT=30 # Graph Server (for future use) GRAPH_SERVER_URL=http://localhost:8000 GRAPH_SERVER_TIMEOUT=30

Running

Start all servers in order:

Terminal 1 - LTM Vector Server (Port 8000):

cd LTM python mcp_server/server_streamable_http.py

Terminal 2 - ZepAI FastMCP Server (Port 8002):

cd ZepAI/fastmcp_server python server_http.py

Note: This automatically loads the Memory Layer and exposes both FastAPI + MCP on port 8002

Terminal 3 - MCP Aggregator (Port 8003):

cd mcp-aggregator python aggregator_server.py

See

Available Tools

Health & Status

  • health_check() - Check health of all connected servers

  • get_server_info() - Get information about connected servers

Memory Server Tools (Port 8002)

Search

  • memory_search(query, project_id, limit, use_llm_classification) - Search knowledge graph

  • memory_search_code(query, project_id, limit) - Search code memories

Ingest

  • memory_ingest_text(text, project_id, metadata) - Ingest plain text

  • memory_ingest_code(code, language, project_id, metadata) - Ingest code

  • memory_ingest_json(data, project_id, metadata) - Ingest JSON data

  • memory_ingest_conversation(conversation, project_id) - Ingest conversation

Admin

  • memory_get_stats(project_id) - Get project statistics

  • memory_get_cache_stats() - Get cache statistics

LTM Vector Server Tools (Port 8000)

Repository Processing

  • ltm_process_repo(repo_path) - Process repository for vector indexing

Vector Search

  • ltm_query_vector(query, top_k) - Query vector database for semantic code search

  • ltm_search_file(filepath) - Search for specific file in vector database

File Management

  • ltm_add_file(filepath) - Add file to vector database

  • ltm_delete_by_filepath(filepath) - Delete file from vector database

  • ltm_delete_by_uuids(uuids) - Delete vectors by UUIDs

Code Analysis

  • ltm_chunk_file(file_path) - Chunk file using AST-based chunking

Testing

1. Check Server Health

curl http://localhost:8003/mcp/sse

2. Access OpenAPI Docs

http://localhost:8003/docs

3. Test a Tool via MCP

# Using MCP client mcp-client http://localhost:8003/mcp health_check

Configuration

Environment Variables

Variable

Default

Description

AGGREGATOR_HOST

0.0.0.0

Aggregator server host

AGGREGATOR_PORT

8003

Aggregator server port

MEMORY_SERVER_URL

http://localhost:8002

Memory server URL

MEMORY_SERVER_TIMEOUT

30

Memory server timeout (seconds)

GRAPH_SERVER_URL

http://localhost:8000

Graph server URL

GRAPH_SERVER_TIMEOUT

30

Graph server timeout (seconds)

LOG_LEVEL

INFO

Logging level

MAX_RETRIES

3

Max retries for failed requests

RETRY_DELAY

1

Delay between retries (seconds)

HEALTH_CHECK_INTERVAL

30

Health check interval (seconds)

Adding New Backend Servers

To add a new backend server (e.g., Graph Server):

  1. Update :

GRAPH_SERVER_URL = os.getenv("GRAPH_SERVER_URL", "http://localhost:8000") GRAPH_SERVER_TIMEOUT = int(os.getenv("GRAPH_SERVER_TIMEOUT", "30"))

  1. Update :

class AggregatorClients: def __init__(self): # ... existing clients ... self.graph_client = MCPServerClient( "Graph Server", config.GRAPH_SERVER_URL, config.GRAPH_SERVER_TIMEOUT )

  1. Add tools in :

@mcp.tool() async def graph_query(cypher: str) -> Dict[str, Any]: """Query Neo4j graph database""" clients = await get_clients() return await clients.graph_client.proxy_request( "POST", "/query", json_data={"cypher": cypher}, retries=config.MAX_RETRIES )

Troubleshooting

Connection Refused

  • Ensure all backend servers are running

  • Check URLs in .env file

  • Verify ports are not blocked by firewall

Timeout Errors

  • Increase MEMORY_SERVER_TIMEOUT or GRAPH_SERVER_TIMEOUT in .env

  • Check backend server performance

  • Verify network connectivity

Health Check Failing

  • Run health_check() tool to diagnose

  • Check backend server logs

  • Verify backend servers are responding

Development

Project Structure

mcp_aggregator/ ├── aggregator_server.py # Main MCP server ├── config.py # Configuration management ├── mcp_client.py # HTTP clients for backend servers ├── requirements.txt # Python dependencies ├── .env # Environment variables ├── __init__.py # Package initialization └── README.md # This file

Adding Logging

import logging logger = logging.getLogger(__name__) logger.info("Message") logger.error("Error")

Future Enhancements

  • Add Graph/Vector DB server integration

  • Implement caching layer

  • Add request rate limiting

  • Implement server load balancing

  • Add metrics/monitoring

  • Support for server discovery

  • WebSocket support for real-time updates

License

Same as parent project (Innocody)

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Provides a unified MCP interface that proxies requests to multiple backend servers including memory/knowledge graph and vector database services. Enables seamless access to distributed MCP tools through a single endpoint with automatic routing, health monitoring, and retry logic.

  1. Architecture
    1. Features
      1. Installation
        1. Running
          1. Start all servers in order:
        2. Available Tools
          1. Health & Status
          2. Memory Server Tools (Port 8002)
          3. LTM Vector Server Tools (Port 8000)
        3. Testing
          1. 1. Check Server Health
          2. 2. Access OpenAPI Docs
          3. 3. Test a Tool via MCP
        4. Configuration
          1. Environment Variables
        5. Adding New Backend Servers
          1. Troubleshooting
            1. Connection Refused
            2. Timeout Errors
            3. Health Check Failing
          2. Development
            1. Project Structure
            2. Adding Logging
          3. Future Enhancements
            1. License

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