Python Client for MCP Servers (Web Based Platform)

A web-based Python platform for connecting to multiple MCP servers, enabling natural language interactions with databases, file systems, and web services. Connect with pre-built or custom MCP implementations in a unified interface. Future integrations include Google Workspace, Microsoft 365, Slack, Salesforce, and GitHub. Free to use under the MIT license.

🚀 Key Features

• MCP Tool Orchestration: Build and connect powerful LLM tools using standardized messaging protocols
• Flask Web Interface: Interact with AI agents through an intuitive, user-friendly dashboard
• LangChain & LangGraph Integration: Create sophisticated AI workflows with industry-standard frameworks
• Multi-Server Support: Connect to multiple tool servers simultaneously from a single interface
• Dynamic Server Management: Add, configure, and update tool servers at runtime without restarts

🏗️ Architecture Overview

1. Flask Web Application: Modern web interface serving as the command center for your AI tools
2. MultiServerMCPClient: Advanced client that orchestrates connections to multiple tool servers
3. LangChain React Agent: Intelligent decision-making system that chooses the right tools for each task
4. MCP Servers: Specialized microservices that expose domain-specific tools through a standardized protocol

🔧 Getting Started with Python MCP

Installation Options

Option 1: Install from PyPI (Recommended)

The simplest way to install Python MCP Client is via pip:

pip install python-mcp-client

You can find the package on PyPI at: https://pypi.org/project/python-mcp-client/

Option 2: Install from Source

If you want the latest development version or plan to contribute:

1. Clone the repository:

   git clone https://github.com/kernelmax/python-mcp-client.git
   cd python-mcp-client

2. Set up a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set environment variables:

   export OPENAI_API_KEY=your-api-key-here

Running Your AI Tool Platform

1. Start the Flask application:

   python flask_app.py

2. Open your browser and navigate to http://localhost:5008

Option 3: Run with Docker

You can also run Python MCP Client using Docker:

1. Pull the pre-built image from Docker Hub:

   docker pull kernelmax/python-mcp-client

2. Run the container:

   docker run -p 5008:5008 -e OPENAI_API_KEY=your-api-key-here kernelmax/python-mcp-client

3. Open your browser and navigate to http://localhost:5008

Option 4: Run with Docker Compose

For a more convenient setup, you can use Docker Compose:

1. Create a docker-compose.yml file or use the one provided in the repository:

   version: '3'
   services:
     mcp-app:
       image: kernelmax/python-mcp-client
       # or build from source:
       # build: .
       ports:
         - "5008:5008"
       environment:
         - OPENAI_API_KEY=${OPENAI_API_KEY}
       volumes:
         - ./templates:/app/templates
       restart: unless-stopped

2. Set your OpenAI API key in your environment:

   export OPENAI_API_KEY=your-api-key-here

3. Start the service:

   docker-compose up

4. Open your browser and navigate to http://localhost:5008

💡 Natural Language AI Tool Examples

Database Management with Natural Language

# Example natural language query
"Show me all users in the database that registered in the last month"

# How the AI agent processes your request
# 1. The LLM agent interprets the natural language query
# 2. It selects the appropriate database tool
# 3. It generates and executes optimized SQL: "SELECT * FROM users WHERE registration_date >= DATE_SUB(NOW(), INTERVAL 1 MONTH)"
# 4. Results are returned in a human-readable format

Database Creation Through Conversation

# Example natural language command
"Create a new database called customer_analytics"

# How the AI agent executes your request
# 1. The LLM agent processes your instructions
# 2. It selects the database creation tool
# 3. It executes the appropriate command with error handling
# 4. Confirmation is provided with next steps

Intelligent File Operations

# Example natural language request
"List all Python files in the current directory"

# How the AI assistant helps you
# 1. The LLM agent processes your request
# 2. It selects the file system tools
# 3. It intelligently filters results for Python files
# 4. Results are displayed in an organized format

🛠️ Available MCP Tool Servers

MySQL Database Assistant

A powerful AI database interface providing tools for:

• SQL query execution with natural language translation
• Automated table creation, insertion, and data manipulation
• Database management with intelligent suggestions
• Schema visualization and exploration

File System Navigator

An intelligent file system assistant with tools for:

• Context-aware file reading and analysis
• Smart file writing with formatting suggestions
• Automatic file creation with templates
• Directory organization and file discovery

🔮 Future Development Roadmap

• User Authentication: Secure access control with role-based permissions
• Database Engine Expansion: Support for PostgreSQL, MongoDB, and other databases
• Real-time Communication: WebSocket integration for live updates and responses
• Containerized Deployment: Docker compose setup for one-click deployment
• Comprehensive Testing: Extensive test suite for reliability and stability
• Session Persistence: Save and resume conversations with your AI tools

Planned MCP Server Integrations

We plan to expand our MCP server ecosystem with integrations for popular platforms:

• Google Workspace: Connect to Gmail, Google Docs, Google Drive, and Google Calendar
• Microsoft 365: Integrate with Outlook, OneDrive, and Microsoft Teams
• Slack: Send messages, manage channels, and automate workflows
• Salesforce: Query customer data, manage leads, and update records
• Jira: Create and manage issues, track projects, and generate reports
• GitHub: Manage repositories, issues, and pull requests
• Zoho: Connect to Zoho CRM, Zoho Books, and other Zoho applications
• Zendesk: Handle support tickets and customer inquiries
• HubSpot: Manage marketing campaigns and customer relationships
• Notion: Create and update pages, databases, and workspaces

💪 How to Contribute

We enthusiastically welcome contributors of all experience levels! Whether you're fixing a typo, improving documentation, or adding a major feature, your help makes this project better.

Ways to Contribute

• Code contributions: Add new features or fix bugs
• Documentation: Improve explanations, add examples, or fix typos
• Bug reports: Help us identify issues
• Feature requests: Suggest new capabilities
• User experience: Provide feedback on usability
• Testing: Help ensure everything works properly

Getting Started for New Contributors

If you're new to open source or this project, look for issues tagged with good-first-issue or beginner-friendly. These are carefully selected to be accessible entry points.

Need help? Join our community chat or ask questions in the issue you're working on.

Contribution Workflow

1. Fork the repository
2. Clone your fork:

   git clone https://github.com/YOUR-USERNAME/python-mcp-client.git
   cd python-mcp-client

3. Create a feature branch:

   git checkout -b feature/your-feature-name

4. Make your changes
5. Test your changes:

   # Run tests to ensure nothing breaks
   pytest

6. Commit your changes with a clear message:

   git commit -m "Add: clear description of your changes"

7. Push to your branch:

   git push origin feature/your-feature-name

8. Create a pull request with a description of your changes

Code Review Process

All submissions require review before merging:

1. A maintainer will review your PR
2. They may request changes or clarification
3. Once approved, your contribution will be merged

Thank you for contributing to make Python MCP Client better for everyone!

🏷️ Repository Tags and Topics

This project is tagged with the following GitHub topics to improve discoverability:

• mcp - Model Context Protocol implementation
• ai-agent - Artificial intelligence agent architecture
• langchain - LangChain framework integration
• langgraph - LangGraph agent workflows
• python-ai - Python-based artificial intelligence
• llm-tools - Large Language Model tooling
• llm-orchestration - LLM tool orchestration
• ai-assistant - AI assistant capabilities
• language-model-tools - Tools for language models
• agent-framework - Framework for building AI agents
• multi-tool-agent - Agent with multiple tool capabilities
• python-llm - Python LLM integration
• openai-integration - OpenAI model integration
• natural-language-processing - NLP capabilities

If you're forking or referencing this project, consider using these tags for consistency and to help others find related work.

Adding Tags to Your Fork

When working with a fork of this repository, you can add these tags to improve its discoverability:

1. Go to your fork on GitHub
2. Click on the gear icon next to "About" on the right sidebar
3. Enter relevant topics in the "Topics" field
4. Click "Save changes"

Using consistent tagging helps build a connected ecosystem of related projects!

📜 License Information

This project is fully open source and available under the MIT License. This means you are free to:

• Use the code commercially
• Modify the code
• Distribute your modifications
• Use privately
• Sublicense

We believe in the power of open source to drive innovation and make AI tools accessible to everyone. By making this project open source, we encourage collaboration, transparency, and community-driven development.

📬 Contact & Support

For questions, feature requests, or support, please open an issue on GitHub or contact the maintainers directly.