Introduction-to-MCP
Health Warn
- No license — Repository has no license file
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 5 GitHub stars
Code Fail
- exec() — Shell command execution in .CLI_Project/Lib/site-packages/PyInstaller/building/build_main.py
Permissions Pass
- Permissions — No dangerous permissions requested
This project is an educational tutorial and multi-agent orchestration framework designed to teach users how to build MCP servers and clients. It allows different AI models (like Claude, Gemini, and Grok) to interact in a structured, multi-agent chat environment.
Security Assessment
Risk Rating: Medium. The repository contains a failure flag for shell command execution, though this specific alert originates from a vendored PyInstaller library (`.CLI_Project/Lib/site-packages/...`) rather than the core application code. The tool requires API keys to connect to various external AI providers, meaning it inherently makes outbound network requests and transmits conversation data. While the README claims a "SafeSkill" verification badge, this is a self-reported metric and not a substitute for standardized security audits. The project requested no explicitly dangerous system permissions.
Quality Assessment
The project is very new and currently has minimal community traction, reflected by its low star count and unknown CLI compatibility. It is actively maintained, with repository activity as recent as today. A major drawback is the missing license file; despite the README displaying an MIT badge, the actual repository lacks the formal legal documentation required for safe open-source reuse.
Verdict
Use with caution: Although the execution warning stems from a third-party package, the missing legal license, early-stage development, and inherent network activity mean you should review the codebase carefully before integrating it into sensitive workflows.
Focusing on building both MCP servers and clients using the Python SDK to make sure these three core primitives—tools, resources, and prompts—and understand how they integrate with Claude AI, Gemini AI, Copilot and Grok to create powerful applications without writing extensive integration code.
🪐 orch
Multi-Agent Orchestration Framework
Official Reference Implementation for the Model Context Protocol (MCP)
Orchestrate intelligent discussions between AI agents from any provider — guided by a smart Moderator AI.
🛡️ SafeSkill — Trust Layer for AI Tools
orch is SafeSkill verified.
SafeSkill is the leading trust layer for AI tools. It automatically scans every MCP server and AI skill for code exploits, prompt injection, data exfiltration, and hidden backdoors before you install or run it.
- ✅ Scanned & protected by SafeSkill
- ✅ Listed in the SafeSkill registry
- ✅ Safe to use in production
✨ Features
- Multi-Provider Agents — Run agents from Anthropic, xAI (Grok), Google (Gemini), OpenAI, and 100+ others through LiteLLM
- Smart Moderator Engine — An intelligent Moderator AI keeps every discussion productive, on-topic, and goal-oriented
- Turn-Based Group Chat — Realistic “think-tank” simulation with configurable roles
- Persistent Data Lake — Every conversation is automatically logged to SQLite for auditing, replay, and analysis (Capability #98)
- Neural Link WebSocket — Real-time broadcasting to the React GUI (Neural Link Patch)
- Long-term Memory — Persistent associative memory for agents across sessions
- Parallel Execution — Concurrent agent processing for high-speed multi-agent simulations
- Security Auditor — Built-in automated scanning for prompt injection and sensitive data leaks
- WhatsApp Gateway — Real-time broadcast of agent responses to WhatsApp for mobile monitoring
- Data Lake & Fine-Tuning — Generate high-quality JSONL/Alpaca/ChatML training data from discussion history
- Data Analysis Tools — Built-in sentiment analysis, dataset comparison, and spreadsheet cleaning
🚀 Quick Start
1. Clone & Install
git clone https://github.com/RobynAwesome/Introduction-to-MCP.git
cd Introduction-to-MCP
pip install -e .
2. Configure Your AI Team
# Configure agents
orch agents config gemini-pro --provider google --model gemini-1.5-pro
orch agents config grok-mod --provider xai --model grok-beta
# See your roster
orch agents list
3. Launch a Discussion
orch serve launch \
--topic "The future of AI in South African fintech" \
--agents "gemini-pro" \
--moderator "grok-mod" \
--max-rounds 8 \
--parallel
4. Neural Link (Browser Interface)
# Start the AGI Control Plane (API + Neural Link GUI)
orch serve api
The browser will automatically open at http://127.0.0.1:8000, where you can watch the agents' reasoning in real-time.
5. Security Audit & Monitoring
# Test WhatsApp integration
orch whatsapp test --message "Neural Link Stable"
📊 Architecture
🗺️ Roadmap
- Phase 1: Core Multi-Agent Orchestration (✅ Done)
- Phase 2: Advanced Moderator Strategies + Memory (✅ Done)
- Phase 3: Full Tool Use via MCP & WebSocket Link (✅ Done)
- Phase 4: Optimization, Scale & Security (🚀 In Progress - v0.1-alpha)
- Long-term Associative Memory
- Parallel Agent Execution
- WhatsApp Messaging Bridge
- Security Auditor Agent
- ChatML/JSONL Training Data Export
- Sentiment Analysis & Data Comparison Tools
Contributing
See CONTRIBUTING.md — we welcome PRs!
License
MIT © RobynAwesome
Made with ❤️ for the AI agent ecosystem
Protected by SafeSkill
textJust paste the whole thing into your README.md and push. It will look professional, modern, and instantly communicate what orch + SafeSkill are all about.
Want me to also generate:
- A better banner image using Grok Imagine?
- Screenshots/GIFs section?
- Or a dark-mode friendly version?
Just say the word! 🚀
git clone https://github.com/RobynAwesome/Introduction-to-MCP.git
cd Introduction-to-MCP
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e .
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