mcp-client-and-server MCP Server

Overview: What is mcp-client-and-server MCP Server?

The mcp-client-and-server project represents an essential component of the Model Context Protocol (MCP) ecosystem, designed to facilitate seamless communication and data exchange between AI applications like Claude Desktop, Continue, and Cursor. By implementing a resource storage system and providing prompt- and tool-based capabilities, this server ensures that AI tools can interact with specific datasets through a standardized protocol.

🔧 Core Features & MCP Capabilities

Resource Storage System

The mcp-client-and-server project introduces a custom URI scheme (note://) to enable the storage and retrieval of notes. Each note resource is meticulously designed to include essential metadata such as:

Name: Unique identifier for each stored note.
Description: A brief summary that provides context about the content within the note.
MIME Type (text/plain): Ensuring compatibility with various AI applications by adhering to common data exchange standards.

Prompt Generation

The server supports a single prompt command (summarize-notes) which can be used to create summaries of all stored notes. This command includes an optional "style" argument, allowing users to control the level of detail in the generated summary (e.g., 'brief' or 'detailed'). By integrating this feature, AI applications such as Claude Desktop can enhance their summarization abilities and offer more tailored user experiences.

Tool Implementation

One key tool provided by the server is add-note, which enables adding new notes to the storage system. This tool requires string arguments for "name" and "content," updating the server state and notifying connected clients of any resource changes, ensuring real-time data consistency across the network.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the mcp-client-and-server project is built around the core principles of the Model Context Protocol (MCP). This includes:

URIs: Utilizing a custom URI scheme (note://) to interact with individual notes.
Prompts: Implementing a standardized prompt mechanism that can be triggered by AI applications for data processing tasks.
Tools: Providing a set of command-line tools specifically designed for interacting with the server and managing stored resources.

By adhering to these architectural guidelines, developers ensure compatibility across MCP clients, enabling a seamless integration experience regardless of which AI application is being used.

🚀 Getting Started with Installation

macOS Setup

To install the mcp-client-and-server project on Mac OS, update the relevant configuration settings in your local environment. For development purposes or for unpublished servers:

{
  "mcpServers": {
    "mcp-client-and-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/mlrsmith/Library/Mobile Documents/com~apple~CloudDocs/Family_Shared/AI/mcp/mcp-client-and-server",
        "run",
        "mcp-client-and-server"
      ]
    }
  }
}

For published servers, the configuration might differ slightly:

{
  "mcpServers": {
    "mcp-client-and-server": {
      "command": "uvx",
      "args": [
        "mcp-client-and-server"
      ]
    }
  }
}

Windows Setup

On Windows, follow a similar process but ensure you adjust the paths and commands accordingly. For development purposes:

{
  "mcpServers": {
    "mcp-client-and-server": {
      "command": "uv",
      "args": [
        "--directory",
        "%APPDATA%\\Claude\\claude_desktop_config.json",
        "run",
        "mcp-client-and-server"
      ]
    }
  }
}

For published servers:

{
  "mcpServers": {
    "mcp-client-and-server": {
      "command": "uvx",
      "args": [
        "mcp-client-and-server"
      ]
    }
  }
}

💡 Key Use Cases in AI Workflows

Real-Time Note Summarization for Enhanced User Experience

Suppose an AI application needs to provide summaries of user-generated notes. By triggering the summarize-notes prompt, the server generates a detailed or brief summary based on the style argument provided by the client. This integration ensures that AI applications can offer immediate feedback and support without delays.

Automated Note Retrieval for Efficient Data Access

Another scenario involves an AI bot fetching specific notes as part of its conversation flow. By employing the note:// URI scheme, users can request certain notes directly from the server via commands or voice interactions. This feature streamlines data access, ensuring that relevant information is always at hand.

🔌 Integration with MCP Clients

Compatible MCP Clients

Claude Desktop

Resources: ✅
Tools: ✅
Prompts: ✅
Status: Full Support

Continue

Resources: ✅
Tools: ✅
Prompts: ✅
Status: Full Support

Cursor

Resources: ✅
Tools: ✅
Prompts: ❌
Status: Tools Only (No prompt support)

This table highlights the compatibility of mcp-client-and-server with various MCP clients, ensuring a consistent experience across different applications while addressing any limitations.

📊 Performance & Compatibility Matrix

To ensure that your implementation functions as expected, consider running performance tests and validating against this matrix:

Client	mcp-client-and-server
Speed	Fast
Reliability	High
Customization	Fully

This matrix indicates high reliability and fast response times, making it a robust choice for integrating with AI applications.

🛠️ Advanced Configuration & Security

For advanced users, there are several configuration options available. One important aspect is setting up API keys:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration ensures secure communication between your AI application and the server.

❓ Frequently Asked Questions (FAQ)

Q1: How do I start using mcp-client-and-server with my AI application?

A1: Follow the quickstart guide provided in the README to set up your environment. Ensure you have the necessary dependencies installed and configure your MCP clients accordingly.

Q2: Can other AI applications integrate with this server?

A2: Yes, mcp-client-and-server supports multiple MCP clients including Claude Desktop, Continue, and Cursor. Check the compatibility matrix for more information.

Q3: How does the summarize-notes prompt work?

A3: The summarize-notes prompt combines all stored notes into a single summary with optional style parameters to control detail level (brief or detailed). This helps in generating comprehensive overviews quickly.

Q4: What are some common challenges when setting up MCP integration?

A4: Common challenges include URI scheme configuration and ensuring real-time data updates. Detailed troubleshooting guides and community support can help address these issues.

Q5: Can I customize the tools and prompts provided by this server?

A5: Yes, you can extend or modify the existing tools and prompts to better fit your specific use cases, but ensure compatibility with MCP standards.

👨‍💻 Development & Contribution Guidelines

Contributions are highly encouraged. To get started:

Clone the repository: git clone https://github.com/modelcontextprotocol/mcp-client-and-server.git
Install dependencies: Run uv install (where uv is your package management tool).
Code and test: Make changes, run tests (uv test), and contribute back to the project.

🌐 MCP Ecosystem & Resources

Explore additional resources within the broader MCP ecosystem:

MCP Inspector - A powerful tool for debugging MCP protocols.
Model Context Protocol Documentation - Official documentation and guides.

By using mcp-client-and-server, you can significantly enhance your AI applications' capabilities, ensuring smoother interactions with various data sources and tools through the Model Context Protocol.

mcp-client-and-server MCP server