Multi-Server MCP: Concept and Implementation

What is Multi-Server MCP?

Multi-Server MCP is an architectural pattern where multiple independent MCP servers expose different sets of tools, and a single MCP client (or agent) dynamically discovers and invokes tools from all of them using a unified protocol.

Instead of one monolithic MCP server containing all tools, responsibilities are split across multiple servers, each focused on a specific domain.

Simple definition

Multi-Server MCP allows an LLM-driven agent to use tools distributed across multiple MCP servers as if they were a single toolbox.

Why Multi-Server MCP?

Single MCP server works fine for demos. Multi-server MCP is needed for real systems.

Problems with a Single MCP Server

• Tool explosion (too many tools in one place)

• Hard to scale teams independently

• Tight coupling between unrelated tools

• Difficult deployments and versioning

Benefits of Multi-Server MCP

Benefit	Explanation
Separation of concerns	Each server owns a domain
Independent scaling	Heavy tools scale separately
Fault isolation	One server failing doesn't kill others
Team autonomy	Different teams own different MCP servers
Clean agent reasoning	Tools grouped by intent

High-Level Architecture

Core Components Explained

MCP Servers

• Independent processes

• Expose tools + schemas

• Do not know about the LLM

• Can be written in Python, Node.js, etc.

Each MCP server:

• Registers tools

• Handles execution

• Returns structured results

MCP Client

• The central orchestrator.

Responsibilities:

• Connects to multiple MCP servers

• Collects all tool definitions

• Sends tool schemas to the LLM

• Routes tool calls to the correct server

• Aggregates results

The MCP client is stateless glue between LLM and servers.

LLM (Agent Brain)

• Sees all tools from all servers

• Decides:

  • Whether a tool is needed

  • Which tool to call

  • With what parameters

• Never calls servers directly

Tool Discovery in Multi-Server MCP

At startup:

1. MCP client connects to Server A

2. Fetches tool list

3. Connects to Server B

4. Fetches tool list

5. Merges tools into a global tool registry

6. Sends registry to the LLM

Example Global Tool Registry

[
  { "name": "add", "server": "math-server" },
  { "name": "multiply", "server": "math-server" },
  { "name": "web_search", "server": "search-server" },
  { "name": "read_file", "server": "fs-server" }
]

Implementation: Multi-Server MCP (FastMCP + Python)

MCP Server 1 – Math Server

# math_server.py
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Math Server")

@mcp.tool()def 
add(a: int, b: int) -> int:
    return a + b

@mcp.tool()
def multiply(a: int, b: int) -> int:
    return a * b

if __name__ == "__main__":
    mcp.run()

MCP Server 2 – Search Server

# search_server.py
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Search Server")

@mcp.tool()
def search(query: str) -> str:
    return f"Search results for '{query}'"

if __name__ == "__main__":
    mcp.run()

MCP Client Connecting to Multiple Servers

# client.py
from mcp.client.session import ClientSession
from mcp.client.stdio import stdio_client
import asyncio

SERVERS = {
    "math": ["python", "math_server.py"],
    "search": ["python", "search_server.py"]
}

async def main():
    sessions = {}
    for name, cmd in SERVERS.items():
        transport = await stdio_client(cmd)
        session = ClientSession(transport)
        await session.initialize()
        sessions[name] = session
    # Collect tools
    all_tools = {}
    for name, session in sessions.items():
        tools = await session.list_tools()
        for tool in tools:
            all_tools[tool.name] = (name, session)

    print("Available tools:", list(all_tools.keys()))

asyncio.run(main())

Tool Invocation Flow

When the LLM decides:

Call tool: add(a=5, b=3)

The MCP client:

1. Looks up add

2. Finds it belongs to Math Server

3. Sends request only to that server

4. Receives result

5. Returns output to LLM

Multi-Server MCP Agent Loop

This loop may run multiple times, calling different servers in one conversation.

Real-World Use Cases

Use Case	MCP Servers
AI Assistant	Math, Search, Calendar, Email
RAG System	Vector DB, File Reader, Web Fetch
DevOps Agent	Git, CI/CD, Cloud APIs
Enterprise AI	HR tools, Finance tools, CRM tools

Best Practices for Multi-Server MCP

1. One domain per server

2. Keep tools small and deterministic

3. Avoid overlapping tool names

4. Version MCP servers independently

5. Treat MCP servers as microservices

6. Keep MCP client thin and dumb

Multi-Server MCP vs Multi-Agent Systems

MCP Multi-Server	Multi-Agent
Tool distribution	Role distribution
One reasoning brain	Multiple reasoning brains
Deterministic execution	Emergent behavior
Easier to debug	Harder to debug

Multi-Server MCP is about where tools live, not who thinks.

The sample test script is in my github Jayant0516 (Jayant Kumar repository.