🚀 MCP聊天助手
⚡ 一个强大的命令行AI聊天机器人，使用 模型上下文协议（MCP） 和 当地LLM（Ollama） --实现智能、上下文感知的对话，而无需依赖付费API。
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✨ 特性
- 🤖 交互式人工智能聊天机器人（基于CLI）
- 🧠 上下文感知对话处理（RAG风格提示）
- 📄 使用文档检索
@filename - ⚙️ 基于命令的执行使用
/commands - 🔌 通过MCP（模型上下文协议）可扩展的工具架构
- 💻 完全运行 离线 使用Ollama（无API成本）
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🧑💻 技术栈
- 语言： python
- 架构： 模型上下文协议（MCP）
- LLM Ollama（calla3-本地推理）
- CLI框架： 提示工具包
- 环境管理： python dotenv
- 包管理器（可选）： 紫外线
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📸 演示
> what's 1+1?
2
> Tell me about @report.pdf
The report.pdf document contains information about...
> /summarize deposition.md
📦 Prerequisites
Python 3.9+
Ollama installed → https://ollama.com
⚙️ Setup
1️⃣ Configure Environment Variables
Create a .env file in the root directory:
CLAUDE_MODEL=llama3
2️⃣ Install Dependencies
Option 1: Using uv (Recommended)
pip install uv
uv venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
uv pip install -e .
uv run main.py
Option 2: Without uv
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install python-dotenv prompt-toolkit "mcp[cli]==1.8.0" ollama
python main.py
🧠 Usage
💬 Basic Chat
> Hello
📄 Document Retrieval
Use @filename to include document context:
> Tell me about @deposition.md
⚙️ Commands
Execute MCP-defined commands:
> /summarize deposition.md
🏗️ Project Structure
MCP-Chat-Assistant/
│
├── core/
│ ├── chat.py # Core chat logic
│ ├── claude.py # LLM interface (Ollama-based)
│ ├── cli.py # CLI controller
│ ├── cli_chat.py # CLI + MCP integration
│ └── tools.py # MCP tool management
│
├── mcp_client.py # MCP client (tool + resource access)
├── mcp_server.py # MCP server (documents + tools)
├── main.py # Entry point
├── .env # Environment config
└── README.md
⚠️ Note on Tool Execution
This project fully implements the MCP architecture, including tools and resources.
However:
🔹 Ollama (local LLM) does not support native tool-calling
🔹 Tool execution logic is preserved but currently disabled
🔹 System still supports context-based document retrieval (RAG)
👉 This architecture can be extended with OpenAI/Anthropic APIs for full tool execution.
🚀 Future Improvements
🧠 Persistent memory (chat history storage)
🌐 Web UI (React / Next.js frontend)
🔊 Voice input/output integration
☁️ Hybrid mode (local + API-based LLM)
🧩 Advanced MCP tool execution with function-calling LLMs
🎯 Learning Outcomes
Deep understanding of Model Context Protocol (MCP)
Building AI systems with client-server architecture
Implementing RAG (Retrieval-Augmented Generation)
Integrating local LLMs (Ollama) into applications
Handling real-world limitations of LLM tool usage
👨💻 Author
Kuldeep Kolage
🔗 GitHub: https://github.com/Kuldeepkolage
⭐ Support
If you found this project helpful:
⭐ Star this repository
🍴 Fork it
🚀 Build and improve on top of it
💡 This project demonstrates how modern AI systems can be built using local models, structured protocols, and scalable architecture — without relying on paid APIs.
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