mcp-llm-client 0.1.2
Python client, based on fastmcp, for connecting to MCP servers through multiple protocols, specifically designed to work with integrated language models.
Requires Python: >=3.11

# MCP LLM client

![alt text](/doc/images/mcp-llm-client_2.png)

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Python client, based on [`"mcp[cli]"`](https://github.com/modelcontextprotocol/python-sdk), for connecting to MCP servers through multiple protocols, specifically designed to work with integrated language models.

## Table of Contents

* [Overview](#overview)
* [Installation](#installation)
* [Implemented Models](#implemented-models)
* [Implemented Transfer Protocols](#implemented-transfer-protocols)
* [System Requirements](#system-requirements)
* [Usage Example](#usage-example)
* [Version History](#version-history)
* [Project Status](#project-status)
* [License](#license)

## Overview

This package provides a Python interface to connect to MCP servers in an easy, intuitive, and configurable way. It offers a modular architecture that allows for easy extension of new transfer protocols and language models. Currently includes support for HTTPStream and GPT-4 mini, with expansion capability for more options in the future.

## Installation

To install the MCP client, you can use pip:

```bash
pip install mcp-llm-client
```

## Implemented Models

The client currently supports the following language models:

| Model | Technical Description |
| --- | --- |
| gpt4o-mini | Optimized implementation of the GPT-4 model that provides a balance between computational performance and resource efficiency. This model is specifically designed to operate in environments with memory constraints while maintaining superior predictive quality. |

>🚨 **CRITICAL CONFIGURATION NOTE** Currently, this project only work with `gpt4o-mini` llm model.

## Implemented Transfer Protocols

Protocols for communication with MCP servers:

| Protocol | Status | Technical Characteristics |
| --- | --- | --- |
| HTTPStream | Implemented | Asynchronous HTTP-based protocol that enables continuous data streaming. Characterized by low memory consumption and real-time processing capability for partial responses. |
| SSE (Server-Sent Events) | Not Implemented | Unidirectional protocol that allows the server to send multiple updated events through a single HTTP connection. Designed specifically for applications requiring real-time updates from the server. |
| stdio | Not Implemented | Standard input/output interface that facilitates direct communication between processes. Will provide a lightweight alternative for local environments and unit testing. |

>🚨 **CRITICAL CONFIGURATION NOTE** Currently, this project only work with `HTTPStream` protocol.

## Future Development Planning

### Pending Language Models

* Integration of additional language models
* Implementation of dynamic model selection system
* Optimization of model loading and management

### Pending Protocols

* Complete implementation of SSE for better real-time event handling
* Development of stdio interface for local environments
* Performance optimization across all protocols

## System Requirements

### Environmental Configuration

* **`.env` file**: The `.env` file contains the authentication credentials necessary for integration with external services. This file must be created in the project root directory with the following format:

    ```env
    # .env
    # OpenAI Authentication
    OPENAI_API_KEY=<YOUR OPENAI-API-KEY>
    ```

* **`config.json` file**: The `config.json` file defines the configuration of available MCP servers. It must be created in the project root directory with the following structure:

    ```json
    {
        "app_name": "mcp-llm-client",
        "mcp_servers": {
            "example_public_server": {
                "transport": "httpstream",
                "httpstream-url": "http://127.0.0.1:8000/public-example-server/mcp",
                "name": "example-public-server",
                "description": "Example public server."
            },
            "example_private_mcp": {
                "transport": "httpstream",
                "httpstream-url": "http://127.0.0.1:8000/private-example-server/mcp",
                "name": "example-private-server",
                "description": "Example private server with oauth required.",
                "auth": {
                    "required": true,
                    "post_body": {
                        "username": "user",
                        "password": "password"
                    }
                }
            },
            "github": {
                "transport": "httpstream",
                "httpstream-url": "https://api.githubcopilot.com/mcp",
                "name": "github",
                "description": "This server specializes in github operations.",
                "auth": {
                    "required": false,
                    "post_body": null
                },
                "headers": {
                    "Authorization": "Bearer {access_token}"
                }
            }
        }
    }
    ```

    If you need an MCP server to test the code, you can use [simple-mcp-server](https://github.com/rb58853/simple-mcp-server).

### Dependencies

* `Python = ">=3.11"`
* `openai = "^1.68.2"`
* `mcp[cli]`
* `mcp-oauth`

## Usage Example

```python
#example1.py
from mcpclient import open_local_chat
open_local_chat()
```

```python
#example2.py
from mcpclient import Chat
chat: Chat = Chat()
while True:
    query = input("> ")
    if query == "":
        break
    for step in chat(query):
        print(f"<< {step.json}")
```

Alternatively, you may test this service using the following [template available on GitHub](https://github.com/rb58853/template_mcp_llm_client):

```shell
# clone repo
git clone https://github.com/rb58853/template_mcp_llm_client.git

# change to project dir
cd template_mcp_llm_client

# install dependencies
pip install -r requirements.txt

# open in vscode
code .
```

## Version History

### Last Version Features

* 💬 Fully functional chat by passing a query; see [`Chat`](./src/mcpclient/services/llm/chat/chat.py).
* ⚙️ Integration with `Tools`, `Resources`, and `Prompts` from MCP servers, achieving a well-integrated client workflow with each of these services.
* 🔐 Simple authentication system using [mcp-oauth](https://github.com/rb58853/mcp-oauth) and [this environmental configuration](#environmental-configuration). Also integrate headers authorization.
* 👾 OpenAI GPT as an integrated LLM using the model `"gpt4o-mini"`.
* 📡 Support for the httpstream transport protocol.
* 💻 Easy console usage via [`open_local_chat()`](./src/mcpclient/dev.py); see [example1](#usage-example) for the use case.

[See more in changelog](/CHANGELOG.md)

## Project Status
>
>⚠️ **Important Notice:** This project is currently in active development phase. As a result, errors or unexpected behaviors may occur during usage

## License

MIT License. See [`license`](LICENSE).