MSG - MCP Swarm Gateway

Universal MCP Bridge + Multi-Agent Swarm Orchestrator

Turn any web-based AI (ChatGPT, Claude, Grok, Kimi, DeepSeek, Gemini) into a full-powered agent with access to your local PC, terminal, browser, and code execution - with a swarm of parallel agents validated in real-time.

What Makes MSG Different

Feature	MCP SuperAssistant	Other Tools	MSG
Web AI Chat Support	11 platforms	2-5	11+ platforms
Local MCP Tools	~10	Varies	33 tools, 8 categories
Multi-Agent Swarm	No	No	Yes - parallel agents
Parallel Validation	No (just pytest)	No	Review agents + pytest
Custom Skills/Plugins	No	Limited	Hot-loadable skill system
Web Dashboard	No	No	Real-time monitoring
Browser Extension	Yes	No	Yes (11 platforms)

Architecture

Web AI Chats (ChatGPT, Claude, Grok, Kimi, DeepSeek, Gemini...)
         |
    HTTP/WebSocket (CORS-enabled)
         |
    +----v--------------+---------------+
    |   MSG GATEWAY     |  SWARM ORCH.  |
    |   (FastAPI)       |  (Parallel)   |
    +----v--------------+---------------+
         |
    +----v--------------+
    |  MCP LOCAL TOOLS  |
    |  (33 tools)       |
    +----v--------------+
    |  Your PC          |
    |  Files, Terminal  |
    |  Browser, Code    |
    +-------------------+

Quick Start

1. Install Dependencies

pip install mcp[cli] fastapi uvicorn websockets pyyaml jinja2 psutil httpx

Optional (for browser tools):

pip install playwright
playwright install chromium

2. Run MSG

cd msg
python run.py

The dashboard will be available at:

Dashboard: http://localhost:8765/dashboard
API Docs: http://localhost:8765/docs
Tool Explorer: http://localhost:8765/tools

3. Install Browser Extension

Open Chrome/Firefox extensions page
Enable "Developer mode"
Click "Load unpacked" and select the extension/ folder
The MSG tool panel will appear on supported AI chat sites

4. Connect Your AI Chat

Option A: Browser Extension (Recommended)

The extension automatically injects MCP tool capabilities into:

ChatGPT (chatgpt.com)
Claude (claude.ai)
Grok (grok.x.ai)
Kimi (kimi.ai)
DeepSeek (chat.deepseek.com)
Gemini (gemini.google.com)
Perplexity (perplexity.ai)
Mistral (chat.mistral.ai)
OpenRouter (openrouter.ai)
T3 Chat (t3.chat)

Option B: Direct API

Send tool calls directly from any app:

curl -X POST http://localhost:8765/api/v1/tools/execute_command \
  -H "Content-Type: application/json" \
  -d '{"params": {"command": "ls -la"}}'

Option C: WebSocket (Real-time)

const ws = new WebSocket('ws://localhost:8765/ws');
ws.send(JSON.stringify({
    tool: 'read_file',
    params: {path: '/path/to/file.txt'}
}));
ws.onmessage = (e) => console.log(JSON.parse(e.data));

33 Built-in MCP Tools

Filesystem (8)

Tool	Description
`read_file`	Read text file with optional offset/limit
`write_file`	Write or overwrite a file
`append_file`	Append content to a file
`list_directory`	List files with metadata (size, mtime)
`search_files`	Recursive glob search
`create_directory`	Create directory structure
`delete_path`	Delete file or directory
`get_file_info`	Size, modified time, permissions

Terminal (3)

Tool	Description
`execute_command`	Run shell command with timeout (security sandboxed)
`execute_script`	Run a script file
`start_background_process`	Start daemon process, return PID

Browser (4)

Tool	Description
`browser_visit`	Navigate to URL, get page content
`browser_click`	Click element by selector
`browser_input`	Fill form input
`browser_screenshot`	Capture page screenshot

Code Execution (3)

Tool	Description
`run_python`	Execute Python code in isolated subprocess
`run_javascript`	Execute JS via Node.js
`evaluate_expression`	Evaluate math/code expressions

System (4)

Tool	Description
`get_system_info`	CPU, RAM, disk, OS info
`list_processes`	Running processes
`kill_process`	Kill process by PID
`search_web`	Web search via DuckDuckGo

Git (4)

Tool	Description
`git_status`	Repository status
`git_log`	Commit history
`git_diff`	Current diff
`git_exec`	Run any git command

Docker (4)

Tool	Description
`docker_ps`	List containers
`docker_exec`	Execute in container
`docker_logs`	Container logs
`docker_run`	Run new container

Dev Tools (3)

Tool	Description
`npm_install`	npm install
`pip_install`	pip install
`run_tests`	Run pytest/jest

Swarm Orchestrator

The swarm executes complex projects with parallel validation:

You submit a project: "Build a Python web scraper"
Project Manager decomposes it into tasks
Code Agents (parallel) write the code
Review Agents (parallel) validate each batch - checking syntax, docstrings, error handling
Verdict: PASS / WARNING / REVISE
Debug Agent fixes REVISE items
Test Agent runs integration tests
Results delivered

Parallel Validation (The Key Feature)

Unlike other systems that only run pytest after code is done, MSG runs Review Agents in parallel with Code Agents. While one agent is still writing code, another is already reviewing what was just completed. This catches issues immediately, not after hours of work.

Submit a Swarm Task

curl -X POST http://localhost:8765/api/v1/swarm/task \
  -H "Content-Type: application/json" \
  -d '{"description": "Build a calculator app", "files": ["calc.py", "test_calc.py"]}'

Check status:

curl http://localhost:8765/api/v1/swarm/task/{task_id}

Configuration

Edit config.yaml:

gateway:
  host: "0.0.0.0"
  port: 8765

security:
  allowed_directories:
    - "/home/yourname/projects"
  blocked_commands:
    - "rm -rf /"
  max_execution_time: 30

swarm:
  max_parallel_agents: 5
  review_strictness: "strict"

Security

Path sandboxing: Tools can only access allowed directories
Command blocklist: Dangerous commands are rejected
Execution timeouts: No infinite hangs
Output limits: Prevents memory exhaustion
Optional auth: Token-based API authentication

Tech Stack

Component	Technology
MCP Server	`mcp` (FastMCP) Python SDK
Gateway	FastAPI + WebSockets
Process Mgmt	asyncio + subprocess
Browser	Playwright
Message Bus	asyncio Queue
Dashboard	FastAPI + Jinja2 + vanilla JS
Extension	Vanilla JS (content script)

Project Stats

34 Python files, 3,693 lines of code
33 MCP tools across 8 categories
5 agent types in the swarm (coder, reviewer, tester, debugger + orchestrator)
11 AI platforms supported via browser extension
6 dashboard pages with real-time WebSocket updates
100% syntax clean - all files pass py_compile

License

MIT - Use it, modify it, make money with it. Go get 'em.

MSG - MCP Swarm Gateway

MSG - MCP Swarm Gateway

What Makes MSG Different

Architecture

Quick Start

1. Install Dependencies

2. Run MSG

3. Install Browser Extension

4. Connect Your AI Chat

Option A: Browser Extension (Recommended)

Option B: Direct API

Option C: WebSocket (Real-time)

33 Built-in MCP Tools

Filesystem (8)

Terminal (3)

Browser (4)

Code Execution (3)

System (4)

Git (4)

Docker (4)

Dev Tools (3)

Swarm Orchestrator

Parallel Validation (The Key Feature)

Submit a Swarm Task

Configuration

Security

Tech Stack

Project Stats

License

Reviews