π§ mcp_server-client_EAG-S-4 - Local Setup
LocalAbout π§ mcp_server-client_EAG-S-4 - Local Setup
A local Python MCP server and client example for users who need math, image, and Pinta automation tools. Its 28 tools combine computations with basic image and drawing operations.
Key features
- 0128 computation and automation tools
- 02Math operations and sequence generation
- 03Character conversion and image thumbnails
- 04Pinta drawing and text operations
Use cases
- 01Run arithmetic, trigonometric, and sequence calculations
- 02Convert characters to ASCII values
- 03Generate thumbnails from image files
- 04Draw shapes and add text through Pinta
MCP support
The MCP server exposes 28 computation, image, and Pinta tools. Its companion client connects to the server and invokes them.
Configuration
Local server config is the same for every client. Copy it into your clientβs MCP config file.
We havenβt collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEπ§ mcp_server-client_EAG-S-4 - Local Setup
This repository provides a minimal implementation to set up and interact with the MCP (Modular Computation Protocol) locally. It consists of a server that exposes various computational tools and a client that communicates with the server to invoke those tools.
The project is managed using uv, a fast Python package manager and workflow tool.
π Project Structure
mcp_server.pyβ Hosts the MCP server with 28 tools available for interaction.mcp_client.pyβ The entry point of the project; runs a client that connects to the MCP server and calls its tools.pyproject.tomlβ Project configuration and dependencies using PEP 621.uv.lockβ Auto-generated lockfile for reproducible installations usinguv.token.envβ Environment file that likely contains secrets or tokens.README.mdβ Project documentation."""
π οΈ Available Tools on the MCP Server
π’ Math Tools
add(a, b)β Add two numbersadd_list(l)β Add all numbers in a listsubtract(a, b)β Subtract two numbersmultiply(a, b)β Multiply two numbersdivide(a, b)β Divide two numberspower(a, b)β Raise a to the power of bsqrt(a)β Square rootcbrt(a)β Cube rootfactorial(a)β Factoriallog(a)β Natural logarithmremainder(a, b)β Modulus operationsin(a)β Sinecos(a)β Cosinetan(a)β Tangentmine(a, b)β Special mining toolint_list_to_exponential_sum(int_list)β Sum of exponentials of integersfibonacci_numbers(n)β First n Fibonacci numbers
π§ String/Image Tools
strings_to_chars_to_int(string)β Convert characters to ASCIIcreate_thumbnail(image_path)β Generate a thumbnail from an image
ποΈ Pinta Automation Tools
open_pinta_application()β Launch and focus Pintaselect_rectangle_tool()select_text_tool()select_circle_tool()draw_rectangle(x1, y1, x2, y2)write_text_inside_rectangle(text, x1, y1, x2, y2)draw_circle(x1, y1, radius)get_lines_of_rectangle(x1, y1, x2, y2)β Return lines from rectangle coordinatesget_midpoint_of_line(x1, y1, x2, y2)β Return midpoint of a line
π Getting Started
1. Clone the Repository
git clone https://github.com/your-username/mcp-protocol.git
cd mcp-protocol
2. Install uv (if not already installed)
curl -Ls https://astral.sh/uv/install.sh | sh
Verify installation:
uv --version
3. Set Up the Project Environment
uv venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
uv pip install -r pyproject.toml
If
requirements.txtdoesn't exist:uv pip freeze > requirements.txt
4. Test MCP Server with GUI client
mcp dev mcp_server.py
5. Run the Client
python mcp_client.py
π Notes
- Ensure Pinta is installed if you're using Pinta-related tools.
- Image tools like
create_thumbnailrequire valid image paths. mcp_client.pyis the main entry point to interact with the protocol.
π License
MIT License. Feel free to fork, modify, and contribute.
π€ Contributing
Pull requests are welcome. For major changes, please open an issue first.
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.