Mcp Client

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{:target=“_blank”} 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

Clone the repository:

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

Navigate to the Project Directory After cloning the repository, move to the project directory:
```
cd mcp-client
```
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{:target=“_blank”} file.

You can also set the provider and model in the mcp-server-config.json{:target=“_blank”} 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{:target=“_blank”} file. You can get the free BRAVE_API_KEY from Brave Search API.

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?

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