Simple REST API 및 CLI 클라이언트는 Model Context Protocol (MCP) 서버와 상호 작용합니다.

키 기능

##1. MCP 호환 서버

• 모든 MCP 호환 서버 서버를 지원합니다.
• 사전 설정된 기본 서버:
• ** SQLite** (test.db는 샘플 제품 데이터로 제공되었습니다) -브라질 검색
• 추가 MCP 서버는 mcp-server-config.json 파일에 추가될 수 있습니다.

##2. LangChain과 통합

• LLM 프롬프트를 실행하기 위해 LangChain 레버리지.
• 여러 MCP 서버를 컴파일하고 특정 쿼리에 대응할 수 있습니다.

##3. LLM 공급자 지원

• 기능 기능을 가진 API를 지원하는 어떤 LLM 공급자도 호환이 되는.
• 예제:
• **오픈 ** -클래드
• **Gemini **
• **AWS Nova **
• **Groq **
• **올라마 **
• 모든 LLM 공급자는 기능 기반 API를 제공하면서도 지원됩니다. 자세한 내용은 langchain 문서를 참조하세요.

설치

1. 저장소를 복제:

2. ** 프로젝트 디렉토리에 네비게이트 ** 저장소 복제 후, 프로젝트 디렉토리로 이동:

3. OpenAI API KEY 환경 변수 설정:

mcp-server-config.json 파일에서 OPENAI_API_KEY을 설정할 수 있습니다.

MCP REST API and CLI Client

A simple REST API and CLI client to interact with Model Context Protocol (MCP) servers.

Key Features

1. MCP-Compatible Servers

• Supports any MCP-compatible servers servers.
• Pre-configured default servers:
  • SQLite (test.db has been provided with sample products data)
  • Brave Search
• Additional MCP servers can be added in the mcp-server-config.json file

2. Integrated with LangChain

• Leverages LangChain to execute LLM prompts.
• Enables multiple MCP servers to collaborate and respond to a specific query simultaneously.

3. LLM Provider Support

• Compatible with any LLM provider that supports APIs with function capabilities.
• Examples:
  • OpenAI
  • Claude
  • Gemini
  • AWS Nova
  • Groq
  • Ollama
  • Essentially all LLM providers are supported as long as they provide a function-based API. Please refer langchain documentation for more details.

Setup

1. Clone the repository:

   git clone https://github.com/rakesh-eltropy/mcp-client.git
   

2. Navigate to the Project Directory After cloning the repository, move to the project directory:

   cd mcp-client
   

3. Set the OPENAI_API_KEY environment variable:

   export OPENAI_API_KEY=your-openai-api-key
   

   You can also set the OPENAI_API_KEY in the mcp-server-config.json file.

   You can also set the provider and model in the mcp-server-config.json file. e.g. provider can be ollama and model can be llama3.2:3b.

4.Set the BRAVE_API_KEY environment variable:

export BRAVE_API_KEY=your-brave-api-key

You can also set the BRAVE_API_KEY in the mcp-server-config.json file. You can get the free BRAVE_API_KEY from Brave Search API.

5. Running from the CLI:

   uv run cli.py
   

   To explore the available commands, use the help option. You can chat with LLM using chat command. Sample prompts:

     What is the capital city of India?
   

     Search the most expensive product from database and find more details about it from amazon?
   

6. Running from the REST API:

   uvicorn app:app --reload
   

   You can use the following curl command to chat with llm:

   curl -X POST -H "Content-Type: application/json" -d '{"message": "list all the products from my local database?"}' http://localhost:8000/chat
   

   You can use the following curl command to chat with llm with streaming:

   curl -X POST -H "Content-Type: application/json" -d '{"message": "list all the products from my local database?", "streaming": true}' http://localhost:8000/chat