π§ mcp_server-client_EAG-S-4 - Local Setup
@devdastl
About π§ mcp_server-client_EAG-S-4 - Local Setup
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Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"local_mcp_server-client_EAG-S-4": {
"command": "uv",
"args": [
"venv"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is π§ 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 28 computational tools and a client that communicates with the server to invoke those tools. The project is managed using the uv Python package manager.
How to use π§ mcp_server-client_EAG-S-4 - Local Setup?
Clone the repository, install uv, create a virtual environment, install dependencies from pyproject.toml, then run mcp dev mcp_server.py to test the server or python mcp_client.py to run the client.
Key features of π§ mcp_server-client_EAG-S-4 - Local Setup
- 28 tools including math, string/image, and Pinta automation
- Uses
uvfor fast dependency management and reproducibility - Server exposes tools that a client can invoke remotely
- Includes a ready-to-use client entry point
- Pinta automation tools for drawing rectangles, circles, and text
- Image thumbnail generation and ASCII conversion tools
Use cases of π§ mcp_server-client_EAG-S-4 - Local Setup
- Perform mathematical calculations via a remote tool server
- Automate Pinta image editing tasks programmatically
- Generate thumbnails or convert characters to ASCII values
- Explore modular computation protocol interactions locally
FAQ from π§ mcp_server-client_EAG-S-4 - Local Setup
What does this project do?
It is a minimal implementation of an MCP server and client, allowing a client to call 28 different tools hosted by the server.
What runtime or dependencies are required?
Python 3, uv package manager, and the dependencies listed in pyproject.toml. For Pinta tools, the Pinta application must be installed separately.
How do I run the client?
After setting up the environment, run python mcp_client.py from the project root. The server can be tested with mcp dev mcp_server.py.
Are there any limitations or known caveats?
Pinta automation tools require Pinta to be installed and running. Image tools like create_thumbnail need valid image file paths.
What license is this project under?
MIT License.
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