npx mcpbar@latest edit -c claude

MCP Demo

This is a basic MCP server implementation, that exposes data and actions for a connected large language model to use.

Example usage for ChatGPT

Give the following instructions to ChatGPT after starting the server

- You are connected to a remote tool MCP Demo. 
- I will describe the usage of functions it contains, the schemas for each function's arguments, and the expected return format.

1. resources/list: Get a list of available resources. Takes no arguments, returns an array of resources with URIs and MIME types

2. tools/list: Get a list of available tools. Takes no arguments, returns an array of tool names

3. tools/call : Use a tool. Required parameters: 'name': The string name of the tool you want to use, 'params': A dictionary representing the tool's arguments

4. prompts/get: Retrieve a prompt. Required parameter: 'name': The string name of the prompt you want to retrieve, returns a string of the prompt text

Thank you, and welcome to MCP Demo

Get Started

Installing via Smithery

To install MCP Demo for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @PawNzZi/aidaily --client claude

Resources

The MCP Demo includes example resources that can be queried:

resources = [
  {"name": "Hello World", "uri": "text://hello-world", "mimeType": "text/plain"},
  {"name": "Introduction to Large Language Models", "uri": "text://introduction-to-llms", "mimeType": "text/plain"}
]

A line from an Introduction to Large Language Models

1. History: Large Language Models (LLMs) trace their roots to early research in artificial neural networks

The returned JSON-encoded response of the tools/list call should look something like:

{"jsonrpc":"2.0","id":1,"result":[{"name":"Example Tool","input":"Prompt","output":"Reply"}]}

Currently only a small set of actions and data is available but we plan to expand this with more exciting capabilities in the future!

Installation

Ensure python is installed on the system and then do the following:

git clone THIS_REPOSITORY

pip install . 

Setup the .env with an API_KEY="YOUR_KEY"

Run

Run the server with

python3 -m mcp_server

The server listens on port 8080