knapsack_problem
Solve a knapsack problem using a genetic algorithm.
LABSCO SUMMARY
An optimiser the assistant can call, instead of code you write each time.
The useful shape here is that a problem is data: a population size, a chromosome size, a generation count and a fitness function, all in JSON. That makes it easy to hand the assistant a variation and ask it to compare runs. Start from one of the bundled samples and edit it — the travelling-salesman file shows exactly which keys the solver expects.
What it is
A Python MCP server that solves maximization problems with a genetic algorithm, driven either from an MCP client or straight from the command line with a JSON problem file.
What you get
- Maximization problems solved by genetic algorithm, defined as JSON rather than code
- A problem definition that takes
population_size,chromosome_size,generationsand a fitness-function block - Sample problems that ship with the repo, including
traveling_salesmanwith its cities and distance matrix - A command-line path — run the algorithm against a problem file directly — so you can check a definition before wiring the server into a client
Requirements
Uv. Run uv run main.py from the genetic-mcp-server directory, and point your client at that directory in the mcpServers block.
Setup effort
Build from source
USE CASES
01
Routing a set of stops
Copy the travelling-salesman sample, swap in your cities and distance matrix, and run it.
02
Seeing how much the settings matter
Run the same problem again with a larger population or more generations and compare the results.
03
Checking a problem file before connecting it
Run it from the command line first, so a malformed definition fails in your terminal rather than mid-conversation.
2 tools
Listed in project code.
traveling_salesman_problem
Solve a traveling salesman problem using a genetic algorithm.
BEFORE YOU INSTALL
What to know first
Tools you get
2 tools registered in the project's code.
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
You clone and build this one yourself.
Server setup
TERMINAL
git clone https://github.com/jbmattos03/genetic_mcp
cd genetic_mcp/genetic-mcp-server
uv run main.pyMCP Clients
Cursor
CURSOR · JSON
{
"mcpServers": {
"genetic_algorithm_mcp_server": {
"command": "uv",
"args": [
"--directory",
"<YOUR_FULL_PATH_TO_GENETIC_MCP>",
"run",
"main.py"
]
}
}
}
