TcpSocketMCP
Enables AI models to interact directly with network services via raw TCP sockets, supporting multiple concurrent connections, buffering, and automated responses.
README
TCP Socket MCP Server
A Model Context Protocol (MCP) server that provides raw TCP socket access, enabling AI models to interact directly with network services using raw TCP Sockets. Supports multiple concurrent connections, buffering of response data and triggering automatic responses.
Motivation and Context
Many network services and IoT devices communicate via raw TCP protocols that aren't covered by existing HTTP-based MCP servers. TcpSocketMCP enables:
- Direct interaction with embedded devices and IoT systems
- Network protocol debugging and testing
- Legacy system integration without HTTP wrappers
- Protocol reverse engineering and analysis
- Automated responses via trigger patterns (useful for IRC, telnet, custom protocols)
This addresses the need for low-level network access that several community members have expressed, particularly for industrial automation, IoT development, and network security testing scenarios.
Demo
Interrogating a device to figure out what it is
Sending data to the device
Sample output from TCP interactions
Installation & Setup
Install from PyPI
# Install with pip
pip install TcpSocketMCP
# Install with uv (recommended)
uv add TcpSocketMCP
# Add to Claude Code (recommended)
claude mcp add rawtcp -- uvx TcpSocketMCP
For Claude Desktop
Add the server to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Option 1: Using installed package (recommended)
{
"mcpServers": {
"tcp-socket": {
"command": "TcpSocketMCP",
"env": {}
}
}
}
Option 2: From source
{
"mcpServers": {
"tcp-socket": {
"command": "python",
"args": ["/path/to/tcp-socket-mcp/run.py"],
"env": {}
}
}
}
Development Setup
# Clone the repository
git clone https://github.com/kaseyk/tcp-socket-mcp.git
cd tcp-socket-mcp
# Install with uv (recommended)
uv pip install -e .
# Or install with pip
pip install -e .
# Run the server directly
python run.py
# Or use the command
TcpSocketMCP
Available Tools
Once configured via MCP, the following tools become available to the AI model:
Core Connection Tools
tcp_connect
Opens a TCP connection to any host:port
- Returns a
connection_idfor subsequent operations - Supports custom connection_id for pre-registered triggers
- Example:
tcp_connect("example.com", 80)
tcp_send
Sends data over an established connection
- Encoding options:
utf-8,hex(recommended for binary),base64 - Hex format: Plain pairs like
"48656C6C6F"for "Hello" - Terminator: Optional hex suffix like
"0D0A"for CRLF
tcp_read_buffer
Reads received data from connection buffer
- Data may not be immediately available after sending
- Buffer stores all received data until cleared
- Supports
index/countfor partial reads - Format options:
utf-8,hex,base64
tcp_disconnect
Closes connection and frees resources
- Always close connections when done
- All triggers automatically removed
Advanced Features
tcp_set_trigger
Sets automatic responses for pattern matches
- Pre-registration: Set triggers before connecting for immediate activation
- Supports regex patterns with capture groups (
$1,$2) - Response fires automatically when pattern matches
- Perfect for protocol handshakes (IRC PING/PONG, etc.)
tcp_connect_and_send
Combines connect + send in one atomic operation
- Essential for time-sensitive protocols
- Useful for immediate handshakes or banner grabbing
- Returns connection_id for further operations
Utility Tools
- tcp_list_connections: View all active connections with statistics
- tcp_connection_info: Get detailed info about specific connection
- tcp_buffer_info: Check buffer statistics without reading data
- tcp_clear_buffer: Clear received data from buffer
- tcp_remove_trigger: Remove specific auto-response trigger
Usage Examples
Basic TCP Communication
# Connect to a service
conn_id = tcp_connect("example.com", 80)
# Send data (hex encoding recommended for protocols)
tcp_send(conn_id, "474554202F20485454502F312E310D0A", encoding="hex") # GET / HTTP/1.1\r\n
# Read response (may need to wait for data)
response = tcp_read_buffer(conn_id)
# Clean up
tcp_disconnect(conn_id)
Automated Protocol Handling
# Pre-register trigger for IRC PING/PONG
tcp_set_trigger("irc-conn", "ping-handler", "^PING :(.+)", "PONG :$1\r\n")
# Connect with pre-registered triggers
tcp_connect("irc.server.com", 6667, connection_id="irc-conn")
# PING responses now happen automatically!
Working with Binary Protocols
# Use hex encoding for precise byte control
tcp_send(conn_id, "0001000400000001", encoding="hex") # Binary protocol header
# Read response in hex for analysis
response = tcp_read_buffer(conn_id, format="hex")
Important Notes
Hex Encoding for Line Endings
Many text protocols (HTTP, SMTP, IRC) require specific line endings. Use hex encoding to avoid JSON escaping issues:
# Common hex sequences:
# 0D0A = \r\n (CRLF) - HTTP, SMTP, IRC
# 0A = \n (LF) - Unix line ending
# 0D0A0D0A = \r\n\r\n - HTTP header terminator
# 00 = Null byte - Binary protocols
Timing Considerations
- Network responses aren't instant - use
tcp_buffer_infoto check for data - Consider implementing retry logic with small delays
- Buffer accumulates all received data - clear when needed
🧪 Testing & Quality
TcpSocketMCP maintains enterprise-grade quality through comprehensive testing:
Test Coverage
- 85% Coverage (exceeds 80% target)
- 80+ Comprehensive Tests across all components
- Cross-Platform Testing (Ubuntu, Windows, macOS)
- Python 3.10-3.12 Support
Quality Gates
- Automated CI/CD with GitHub Actions
- Security Scanning with Bandit and Safety
- Code Quality Analysis with Ruff and MyPy
- Performance Monitoring and complexity analysis
Running Tests Locally
# Install with test dependencies
uv pip install -e .
uv pip install pytest pytest-asyncio pytest-cov
# Run full test suite with coverage
uv run pytest tests/ --cov=src/TcpSocketMCP --cov-report=term-missing
# Quick test run
uv run pytest
See TESTING.md for comprehensive testing documentation.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.