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https://github.com/rakesh-eltropy/mcp-client
https://github.com/rakesh-eltropy/mcp-client
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/rakesh-eltropy/mcp-client
- Owner: rakesh-eltropy
- Created: 2024-12-04T15:32:29.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-12T21:00:03.000Z (about 2 months ago)
- Last Synced: 2024-12-12T22:17:56.105Z (about 2 months ago)
- Language: Python
- Size: 28.3 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-mcp-servers - mcp-client
README
# MCP REST API and CLI Client
A **simple REST API** and **CLI client** to interact with [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) servers.
## Key Features
### 1. MCP-Compatible Servers
- Supports any [MCP-compatible servers](https://github.com/punkpeye/awesome-mcp-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](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](https://python.langchain.com/docs/integrations/chat/) for more details.
## Setup
1. Clone the repository:
```bash
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:
```bash
cd mcp-client
```
3. Set the OPENAI_API_KEY environment variable:
```bash
export OPENAI_API_KEY=your-openai-api-key
```
You can also set the `OPENAI_API_KEY` in the [mcp-server-config.json](mcp-server-config.json) file.
You can also set the `provider` and `model` in the [mcp-server-config.json](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:
```bash
export BRAVE_API_KEY=your-brave-api-key
```
You can also set the `BRAVE_API_KEY` in the [mcp-server-config.json](mcp-server-config.json) file.
You can get the free `BRAVE_API_KEY` from [Brave Search API](https://brave.com/search/api/).
5. Running from the CLI:
```bash
uv run cli.py
```
To explore the available commands, use the `help` option. You can chat with LLM using `chat` command.
Sample prompts:
```bash
What is the capital city of India?
```
```bash
Search the most expensive product from database and find more details about it from amazon?
```
6. Running from the REST API:
```bash
uvicorn app:app --reload
```
You can use the following curl command to chat with llm:
```bash
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:
```bash
curl -X POST -H "Content-Type: application/json" -d '{"message": "list all the products from my local database?", "streaming": true}' http://localhost:8000/chat
```
## Contributing
Feel free to submit issues and pull requests for improvements or bug fixes.