Included with KlipIDE Pro

Stop wasting RAM & tokens
on duplicate MCP servers

Share MCP server processes across all terminals. One gateway, O(1) memory growth.

No Docker required
98% token savings
94% fewer processes
Hot configuration

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI assistants use external tools—just like Docker lets apps use external services.

  • Claude, Cursor, IDX can query your GitHub repos, databases, or browse the web
  • MCP Servers provide these tools via stdio, HTTP, or Docker containers
  • Each terminal traditionally spawns its own server processes—that's the problem

Think of MCP Gateway as nginx for AI tools—one gateway routing to shared backend processes.

The Problem

Traditional MCP: Massive Duplication

Each terminal spawns its own MCP server processes. With 7 terminals using 2 servers each, you're running 14 duplicate processes.

Worse: traditional MCP sends full list_tools definitions (27,000+ tokens) with every single message—even if you never use most tools.

1
Each Terminal Spawns Duplicate Processes
Every MCP server runs as a separate process per terminal — even identical ones
Activity Monitor — MCP Processes
Memory: 1.11 GB used by MCP
PID
Process Name
Terminal
Memory
CPU
1842
mcp-supabase
T1
52 MB
0.3%
1843
mcp-context7
T1
48 MB
0.1%
1844
mcp-filesystem
T1
35 MB
0.2%
1845
mcp-memory
T1
28 MB
0.1%
1901
mcp-supabase
T2
52 MB
0.2%
1902
mcp-context7
T2
48 MB
0.1%
...
+ 22 more duplicate processes
T3-T7
...
...
7 terminals × 4 MCP servers = 28 duplicate processes that could be shared
28
Processes
~1.1 GB
RAM Wasted
2
Every Message Sends the Full Tool List
67,500 tokens of MCP tool definitions are sent with every single message
>_
T1
Input
847
+
MCP Tools
67.5k
=
Total
68.3k
>_
T2
Input
3.2k
+
MCP Tools
67.5k
=
Total
70.7k
>_
T3
Input
512
+
MCP Tools
67.5k
=
Total
68.0k
>_
T4
Input
4.9k
+
MCP Tools
67.5k
=
Total
72.4k
>_
T5
Input
1.8k
+
MCP Tools
67.5k
=
Total
69.3k
>_
T6
Input
423
+
MCP Tools
67.5k
=
Total
67.9k
>_
T7
Input
2.8k
+
MCP Tools
67.5k
=
Total
70.3k
What's in those 67.5k tokens?
Supabase18.1k
Context720.0k
Filesystem9.2k
Memory5.8k
Others14.3k
Total
67.5k
7 messages × 67.5k tokens = 487.0k tokens sent (mostly duplicated tool definitions)
487.0k
Tokens/Convo
Total Waste Per Conversation
Resources that could be saved with proper MCP multiplexing
28
Duplicate Processes
~1.1 GB
RAM Wasted
472K
Tokens Wasted
~$1.42
Cost Wasted
Memory Waste

1.4 GB

14 servers × ~100MB each

Token Overhead

27,000+

list_tools tokens per message

Cost Impact

$0.58

wasted per conversation

The Solution

MCP Gateway: Process Sharing

All terminals connect to one lightweight gateway process. The gateway routes requests to shared backend servers—one Supabase process, one GitHub process—no matter how many terminals you open.

Traditional MCP

Active processes:14 servers
Memory usage:1.4 GB
Tokens per message:27,500
Adding 10th terminal:+200 MB

MCP Gateway

Active processes:3 (gateway + 2 servers)
Memory usage:220 MB
Tokens per message:~800
Adding 10th terminal:+0 MB (O(1)!)
1
Smart Process Sharing
Global tools shared across all terminals, workspace-scoped tools isolated by credentials
Activity Monitor — MCP Gateway
Memory: 235 MB total
PID
Process Name
Scope
Serves
Memory
2001
mcp-gateway
Gateway
ALL
20 MB
2010
mcp-context7
Global
T1-T7
48 MB
2011
mcp-filesystem
Global
T1-T7
35 MB
2012
mcp-memory
Global
T1-T7
28 MB
2020
mcp-supabase
WS-A
T1-T4
52 MB
2021
mcp-supabase
WS-B
T5-T7
52 MB
1 gateway + 3 global + 2 workspace-scoped = 6 total processes
6
Processes
235 MB
Total RAM
2
Lazy Tool Loading
Tool definitions only sent when actually needed — no more 67.5k token overhead on every message
Token Flow — Lazy Loading
54.7k total
Terminal
Message
Tool Loaded
Tool Tokens
Total
T1
847
—
0
847
T2
1.2k
Context7
+20.0k
21.3k
T3
512
—
0
512
T4
1.9k
Filesystem
+9.2k
11.1k
T5
756
—
0
756
T6
1.4k
Supabase
+18.1k
19.5k
T7
647
—
0
647
4 msgsno tools
3 msgstool loaded
54.7k
Tokens/Convo
Total With MCP Gateway
Shared processes + lazy tool loading = massive savings
6
Total Processes
↓ 78% vs 28
235 MB
RAM Used
↓ 79% vs 1.1GB
54.7k
Tokens Used
↓ 89% vs 472K
~$0.16
Cost
↓ $1.26 saved

O(1) Memory

Add unlimited terminals without spawning new server processes. Memory stays constant.

Lazy Discovery

Tools load on-demand, not upfront. 98% token reduction vs traditional list_tools bloat.

No Docker Required

Stdio servers work natively. Docker optional for containerized isolation.

Hot Configuration

Add/remove servers without restarting your AI session. Agents can self-configure.

Server Types

Choose the right transport mode for each MCP server

HTTP Mode

Memory~60 MB
Startup~1.5s

Native HTTP servers. Fastest startup, lowest overhead.

Best for: Playwright, Context7

Stdio + Wrapper

Memory~100 MB
Startup~2.5s

Auto-generated HTTP wrapper for stdio servers.

Best for: GitHub, Filesystem, npm packages

Docker

Memory~70 MB
Startup~4s

Containerized isolation for complex dependencies.

Best for: Python servers, security sandbox
Learn more about server types →

O(1) Memory Scaling

Add unlimited terminals without increasing memory usage

5
10
15
20
25
30
Traditional (linear)
Gateway (constant)
O(1)
Memory growth
~235 MB
Fixed overhead
∞
Terminal capacity

How It Works

Simple architecture: one gateway process routes to shared backends

1

Configure AI client

Point Claude Code or Cursor to http://localhost:8989/mcp

2

Stateless routing

Every request includes project or cwd context. Gateway loads that project's .env and routes to the right server.

3

Shared processes

Backend servers spawned once and shared. GitHub server handles all terminals. Supabase server switches credentials per request.

4

Intelligent scoping

Servers scoped globally (filesystem), per-workspace (memory graph), or per-credential (GitHub tokens). Gateway handles isolation automatically.

Included with KlipIDE Pro

MCP Gateway comes pre-configured with KlipIDE Pro. Upgrade to start saving RAM and tokens across all your terminals.

Upgrade to ProView Pricing