OthmaneBlial/term_mcp_deepseek

DeepSeek MCP-like Server for Terminal

This project is an MCP‑like server using the DeepSeek API. It aims to demonstrate the core concepts behind the Model Context Protocol (MCP) by exposing endpoints that allow AI assistants to:

• List available tools.
• Invoke commands on an active shell session.
• Integrate with an AI chat (DeepSeek) that can include special instructions (e.g., CMD: lines) to trigger command execution.

Note: While this implementation captures many of the MCP ideas and includes features like real-time streaming, session management, and basic security, it is not yet a fully compliant MCP server as defined by Anthropic. It is designed as a proof-of-concept, and further enhancements (e.g., complete JSON‑RPC protocol support, advanced authentication, and comprehensive error handling) would be needed for production use.

Features

• Chat Interface: A modern web-based chat client (using Flask and Tailwind CSS) where users can interact with the server, now with real-time updates and improved error handling.

• AI Integration: Uses the DeepSeek API to generate responses. The AI can instruct the server to execute terminal commands by including lines beginning with CMD:.

• Terminal Command Execution: Executes shell commands via a persistent Bash session using the pexpect library and returns output to the client, with added real-time streaming capabilities.

• MCP Endpoints: Provides /mcp/list_tools and /mcp/call_tool endpoints that mimic MCP tool discovery and invocation, with expanded protocol support including prompts, resources, and roots.

• Real-time Streaming: Server-Sent Events (SSE) for live command execution updates and terminal output.

• Security Enhancements: Basic authentication, rate limiting, input validation, and security headers for safer operation.

• Multiple Transport Options: Supports both HTTP REST API and STDIO command-line interface for flexibility.

• Session Management: Improved session handling and conversation storage.

• Docker Support: Containerization for easier deployment and testing.