About Osmosis-MCP-4B
Open source small language model trained for MCP. Integrates with MCP tools including Brave Search, Google Maps, and AccuWeather, supports GUI and TUI interfaces for local model inference.
Key features
- 01Osmosis small language model
- 02Multiple MCP tool integration
- 03Web search and location services
- 04Weather data access
- 05Python code interpreter
- 06GUI and TUI interfaces
Use cases
- 01Local model inference
- 02Comprehensive information queries
- 03Geolocation analysis
- 04Weather forecast applications
MCP support
Osmosis-MCP-4B is an open source model optimized for MCP, connecting to multiple tool servers via MCP protocol to integrate search, maps, and weather capabilities.
Details
From the project READMEOsmosis-MCP-4B
Blog Post
Prerequisites
- Python 3.x
- uvx
- Access to a model server (e.g., running locally via vLLM/lm studio)
- API Keys for:
- Brave Search
- Google Maps
- AccuWeather
Setup & Installation
-
Install LM studio here and use it to run the model from a http endpoint
- find and download model in discover
- select model to load and load an osmosis model
- start the web server with model loaded
- find and download model in discover
-
Clone the repository (if applicable):
git clone https://github.com/Gulp-AI/Osmosis-MCP-4B-demo cd Osmosis-MCP-4B-demo -
Install dependencies: It's recommended to use a virtual environment.
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -r requirements.txt -
Set up Environment Variables: Create a
.envfile in the root of the project directory and add your API keys and model server configuration:BRAVE_API_KEY="your_brave_api_key" GOOGLE_MAPS_API_KEY="your_google_maps_api_key" ACCUWEATHER_API_KEY="your_accuweather_api_key" APP_STYLE="gui" # or "tui"if an api key is not provided, the tool will not be loaded. If
APP_STYLEis omitted, the application defaults to GUI mode. Themain.pyscript currently configures the model server URL directly. Ensurehttp://localhost:1234/v1is the correct endpoint for your Qwen model server (this is what lm studio uses). -
Serve local model: Use a tool like lm studio to provide a usable endpoint.
Environment Variables
The application uses the following environment variables (loaded from a .env file):
BRAVE_API_KEY: Your API key for Brave Search.GOOGLE_MAPS_API_KEY: Your API key for Google Maps.ACCUWEATHER_API_KEY: Your API key for AccuWeather.APP_STYLE: 'tui' or 'gui'
Available Tools (MCP Servers)
The agent is configured to use the following tools via MCP:
- Time: Provides current time information.
- Brave Search: Enables web search capabilities.
- Requires:
BRAVE_API_KEY
- Requires:
- Fetch: Fetches content from URLs.
- Google Maps: Provides location-based services.
- Requires:
GOOGLE_MAPS_API_KEY
- Requires:
- Weather: Provides weather forecasts.
- Requires:
ACCUWEATHER_API_KEY
- Requires:
- Code Interpreter: A built-in tool for executing Python code snippets.
These servers need to be running and accessible for the agent to utilize their respective functionalities. The main.py script provides the commands to start these MCP servers.
How to Run Graphical User Interface (GUI):
This mode launches a web-based interface for interacting with the agent. This is the default mode.
```bash
python main.py
```
The web UI will be accessible at the address provided by the `WebUI` component upon startup (on `http://localhost:7860`).
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.