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An advanced Python orchestrator connecting local Ollama models to 200+ Model Context Protocol (MCP) servers for autonomous cybersecurity and AI agent tasks.
[!WARNING]
LEGAL DISCLAIMER & EDUCATIONAL PURPOSE ONLY
This project and all associated scripts are intended strictly for educational, research, and defensive purposes.
Do not use these tools, scripts, or AI configurations on any network, system, or device for which you do not have explicit, authorized permission.
The creator, contributors, and maintainers of this repository are not responsible for any misuse, damage, or illegal activities caused by the use of this software. Always abide by local, state, and international laws.
Imagine if ChatGPT or Claude had direct access to your computer's terminal, files, and professional software, but it ran 100% locally, offline, and privately on your own hardware. That is exactly what this project does.
This Python orchestrator bridges Ollama (your local AI brain) with the Model Context Protocol (MCP) (your AI's hands). It automatically detects all your installed MCP tools and feeds them into the AI, allowing it to perform complex, multi-step tasks autonomously.
graph TD
User([🧑 You]) -->|Types Command| Agent[🤖 agent.py]
Agent <-->|Asks for reasoning| Ollama[(🧠 Ollama Model)]
Agent <-->|Executes commands| MCP_Hub{🔌 MCP Servers}
MCP_Hub -->|Tool| T1[📁 Filesystem]
MCP_Hub -->|Tool| T2[💻 Terminal/Shell]
MCP_Hub -->|Tool| T3[🛡️ 200+ Custom Tools]
This agent is built with standard Python, meaning you can run it almost anywhere:
Running an AI agent requires RAM. Choose the model that fits your machine:
qwen2.5:7b, llama3.1:8b, or command-r.qwen2.5:0.5b or qwen2.5:1.5b. (Note: The AI will not be exceptionally smart, but it will successfully demonstrate the tool-chaining architecture).Follow these exact steps to get your autonomous agent running from scratch.
You need Ollama to run the AI model locally.
curl -fsSL https://ollama.com/install.sh | sh
Open a terminal and tell Ollama to download a model. For this guide, we will use Qwen 2.5 (7 Billion parameters), which is excellent at using tools.
ollama pull qwen2.5:7b
(If you are on a low-end PC or Raspberry Pi, type ollama pull qwen2.5:0.5b instead).
Clone this repository to your machine and install the required Python libraries.
# 1. Clone the repository
git clone https://github.com/mr-vishal-singh01/Ollama-MCP-Orchestrator.git
cd Ollama-MCP-Orchestrator
# 2. Create a virtual environment (Recommended)
python3 -m venv venv
# 3. Activate the virtual environment
# On Linux/Mac:
source venv/bin/activate
# On Windows:
# .\venv\Scripts\activate
# 4. Install dependencies
pip install mcp openai
The agent looks for a configuration file located at ~/.gemini/settings.json (or wherever your MCP servers are configured). Ensure you have MCP servers installed (like mcp-shell-server or mcp-server-filesystem).
If your settings.json is located somewhere else (like Claude Desktop's config), open agent.py and change the SETTINGS_PATH variable on line 13 to point to your specific config file!
Ensure Ollama is running in the background, then launch the agent:
python agent.py
When you start the agent, it will automatically connect to every MCP server it finds.
When booting up, you will be asked:
Enable AUTO mode? (Skips (y/n) confirmation for tools - WARNING: Powerful but risky) [y/N]:
(y/n) before executing any command on your computer. This is extremely safe.If the agent runs a tool that outputs a massive amount of text (like a heavy directory fuzzing scan), the agent.py script will automatically intelligently truncate the middle of the text. This prevents your local LLM from running out of memory (Context Window Overflow) and crashing!
Created by Vishal Singh | Open Source for the Community
Selected from shared topics, language and repository description—not editorial ratings.
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