MCP Server Go

🚀 MCP Server (Implemented in Go)

The MCP Server is an open - source platform based on the Go language, aiming to assist developers in quickly building intelligent dialogue systems. By providing structured interfaces and pre - trained models, it simplifies the development process of AI applications.

🚀 Quick Start

# Install dependencies
go get github.com/shaneholloman/mcp-server-go/...

# Start the server
go run main.go

✨ Features

Core Concepts

Resources

Resources are the most fundamental concept in the MCP Server, representing any data or content in the system. Resources can be text, images, or other media files. MCP supports the following resource types:

• Text Resources: Store and manage text content.
• Image Resources: Process and store image files.
• Model Resources: Integrate and deploy machine - learning models.

Prompts

Prompts are templates used to guide large language models on how to handle specific tasks. They include dialogue flows, system instructions, and input - output specifications, helping the model complete its goals more efficiently.

Services

Services are encapsulations of a set of functions, responsible for implementing specific business logic. MCP provides a rich set of default services and also allows developers to customize and extend them.

Core Functionalities

Resource Management

• Storage and Retrieval: Quickly locate and obtain the required resources through URIs and MIME types.
• Version Control: Support historical version management of resources for easy backtracking and recovery.
• Security Policies: Provide fine - grained permission control to ensure the security of sensitive data.

Dialogue Protocol

MCP defines a concise and efficient dialogue protocol, standardizing the request format, response structure, and error - handling mechanism. This makes the interaction between different systems more unified and reliable.

Model Integration

• Multi - model Support: Be compatible with mainstream large language models, such as the GPT series and other open - source models.
• Dynamic Loading: Allow switching between different models at runtime to flexibly handle various application scenarios.
• Performance Optimization: Have a built - in caching mechanism and parallel processing capabilities to improve overall operating efficiency.

Use Cases

Dialogue Robots

Rapidly build applications such as intelligent customer service and virtual assistants. Achieve efficient user interaction by configuring appropriate prompts and resources.

Data Processing

Leverage MCP's resource management and model integration capabilities to build automated data - processing workflows, such as text classification and image recognition tasks.

Intelligent Systems

Integrate MCP into existing systems to endow them with intelligent capabilities. For example, in areas such as automatic document generation, code review, and system monitoring.

📚 Documentation

For detailed documentation, please refer to Official Documentation.

💻 Usage Examples

For more example code, please visit the examples/ directory.

🔧 Contribution Guidelines

Development Preparation

• Go Version: 1.23 or higher.
• Clone the Repository: git clone https://github.com/shaneholloman/mcp-server-go.git

Submitting Code

1. Create a new branch:

   git checkout -b my - branch
   

2. Add changes and commit:

   git add . && git commit -m "My changes"
   

3. Push to the remote repository:

   git push origin my - branch
   

Feedback

If you have any questions or suggestions, feel free to create an Issue or Discussion on GitHub.