Mcpcpp
mcpcpp is a modern C++ library for implementing the Model Context Protocol (MCP), supporting the creation of AI application tool integration servers and clients, and providing features such as dual transport modes (STDIO and SSE/HTTP) and dynamic configuration loading.
2.5 points
6.9K

What is mcpcpp?

mcpcpp is a C++ library that implements the Model Context Protocol (MCP) standard. MCP is a protocol that allows AI assistants (such as Claude, ChatGPT, etc.) to safely access external tools and data. Through mcpcpp, you can expose the functions of your C++ application to AI assistants for use, such as calculators, file operations, API calls, etc.

How to use mcpcpp?

Using mcpcpp is very simple: 1) Install the library and dependencies, 2) Create an MCP server, 3) Register your tool functions, 4) Run the server. AI assistants can then call the tools you registered through the MCP protocol.

Applicable scenarios

Suitable for scenarios where AI assistants need to access local or remote services, such as data analysis tools, system management tools, custom API gateways, development assistance tools, etc. It is especially suitable for C++ developers who want to provide high-performance tool services for AI assistants.

Main Features

Full MCP Protocol Support
Fully compatible with the MCP protocol standard (v2024 - 11 - 05), based on JSON - RPC 2.0, ensuring compatibility with various AI clients.
Dual Transport Modes
Supports both STDIO (standard input/output) and SSE/HTTP communication modes to adapt to different deployment environments.
Tool Registration
You can register C++ functions as AI - callable tools, supporting parameter validation and type safety.
Resource Service
You can expose data, configuration files, and documents as resources for AI assistants to read.
Prompt Templates
Provides reusable prompt templates. AI assistants can generate responses in a specific format based on the templates.
Client Library
Includes a complete client library that can connect to other MCP servers from a C++ program.
Dynamic Configuration
Supports dynamically loading tool and resource configurations from a JSON configuration file without recompilation.
Header - Only Option
Provides a header - only version for easy and quick integration into existing projects.
Advantages
High performance: Implemented in C++, with fast execution speed and low memory usage
Type safety: A strong - typed system reduces runtime errors
Cross - platform: Supports mainstream systems such as Linux, macOS, and Windows
Easy to integrate: Provides CMake support and a header - only version
Protocol compatibility: Fully compatible with the official MCP standard
Rich features: Supports complete MCP functions such as tools, resources, and prompts
Limitations
Requires a C++17 or higher compiler
Has a certain learning curve for non - C++ developers
Requires manual management of memory and resources (a C++ feature)
Slightly slower for rapid prototyping compared to Python/JavaScript versions

How to Use

Install mcpcpp
First, clone the repository and build the project. You need to install CMake and a C++17 compiler.
Create an MCP Server
Include the header file in your C++ project and create a server instance.
Register Tool Functions
Register your C++ functions as AI - callable tools. You need to define the tool name, description, and parameter schema.
Configure the AI Client
Configure the MCP server connection in the AI client (such as Claude Desktop).
Run and Test
Run the server and test tool calls in the AI client.

Usage Examples

Mathematical Calculator Tool
Create a mathematical calculator that allows AI assistants to perform various mathematical operations.
File System Browser
Expose file system access functions, allowing AI assistants to read directory contents and file information.
System Monitoring Tool
Provide system status monitoring functions, allowing AI assistants to check CPU and memory usage.
API Gateway Tool
Encapsulate internal APIs as MCP tools, allowing AI assistants to call APIs through natural language.

Frequently Asked Questions

What is MCP? Why is it needed?
What are the differences between mcpcpp and the Python/JavaScript MCP implementations?
Do I need to learn C++ to use mcpcpp?
How to debug an MCP server?
Which AI clients are supported?
How to ensure the security of tool calls?
Can I run multiple MCP servers simultaneously?
How to handle errors in tool calls?

Related Resources

Official MCP Documentation
Official documentation and specifications for the Model Context Protocol
GitHub Repository
mcpcpp source code and issue tracking
Python MCP SDK
Official Python MCP implementation, can be used as a reference
TypeScript MCP SDK
Official TypeScript MCP implementation
Claude Desktop Configuration Guide
How to configure an MCP server in Claude Desktop
JSON Schema Documentation
JSON Schema specification for tool parameter schema definition

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "cppmcp-example": {
      "command": "/path/to/build/examples/simple_server",
      "args": ["stdio"]
    }
  }
}
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