Gurobi MCP
About
Solve optimization problems formulated by an LLM using the on-device Gurobi solver.
Details
- Author
- kkonuru
- Categories
- Developer Tools, AI, Other
- Tags
- #data-analysis
Jump to
How to setup the server in Claude desktop?
Note: If Claude desktop is not already downloaded, download it here:https://claude.ai/download
git clone https://github.com/KKonuru/GurobiMCP.git
or download the project as a zip and unzip in your repo directory
- Enter the project directory, create a virtual environment, and install packages listed in requirements.txt
cd GurobiMCP python -m venv .venv source .venv/bin/activate pip install -r requirements.txt
- Copy the full path of your python interpreter in .venv folder and the main.py file in your package. Then open claude desktop and open settings.
In settings click on the developer tab on the left side of the page. Then click open edit config which will open file explorer.
Open the file named "claude_desktop_config.json". Here modify the file such as stated below but replace the command and args with the two paths copied earlier.
{ "mcpServers": { "gp-solver": { "command": "/Path/to/gurobiMCP/.venv/bin/python", "args": [ "/Path/to/gurobiMCP/main.py" ] } } }
- Restart Claude desktop and the tool should appear after clicking the second icon on the left.The prompt also appears when clicking the + icon
This is a web browser that enables your coding agent, such as Claude Code, to visit websites on your behalf and assist you in identifying bugs or creating UI test cases.
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Model Context Protocol (MCP) is a open protocol that standardizes how Large Language Model (LLM) applications, such as IDEs and AI, connect with external data sources, tools, and prompts. Any application that is MCP compatible can use any MCP server to connect to external sources. This allows AI applications to improve its context with access to external data sources or tools. MCP servers can include databases, research paper search tools, etc. that extends the context available to a LLM.
This is a MCP server that connects a AI application to use gurobi solver on device to solve optimization problems formulated by the LLM. The MCP server runs on device and uses gurobi software installed on device
The MCP server accounts for the following problem types:
- Linear Programming (LP)
- Mixed-Integer Linear Programming (MILP)
- Quadratic Programming (QP) – convex and non-convex
- Mixed-Integer QP (MIQP)
- Quadratically Constrained Programming (QCP) – convex and non-convex
- Mixed-Integer QCP (MIQCP)
How to setup the server in Claude desktop?
Note: If Claude desktop is not already downloaded, download it here:https://claude.ai/download
git clone https://github.com/KKonuru/GurobiMCP.git
or download the project as a zip and unzip in your repo directory
- Enter the project directory, create a virtual environment, and install packages listed in requirements.txt
cd GurobiMCP python -m venv .venv source .venv/bin/activate pip install -r requirements.txt
- Copy the full path of your python interpreter in .venv folder and the main.py file in your package. Then open claude desktop and open settings.
In settings click on the developer tab on the left side of the page. Then click open edit config which will open file explorer.
Open the file named "claude_desktop_config.json". Here modify the file such as stated below but replace the command and args with the two paths copied earlier.
{ "mcpServers": { "gp-solver": { "command": "/Path/to/gurobiMCP/.venv/bin/python", "args": [ "/Path/to/gurobiMCP/main.py" ] } } }
- Restart Claude desktop and the tool should appear after clicking the second icon on the left.The prompt also appears when clicking the + icon
This is a web browser that enables your coding agent, such as Claude Code, to visit websites on your behalf and assist you in identifying bugs or creating UI test cases.
Train a Linear Regression model by uploading a CSV dataset file, demonstrating an end-to-end machine learning workflow.
Integrates with MLflow, enabling AI assistants to interact with experiments, runs, and registered models.
Neo is the first autonomous AI engineering agent that plans, researches and executes multi-step work for tasks such as building AI agents, AI model fine-tuning and evals, and ML pipelines; using your codebase, data, and experiments to ship faster with inspectable artifacts. It can reason over your repository, wire tools and retrieval, debug training runs, and help you develop production-ready AI workflows
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The MCP server provides shape of tensors to convert PyTorch code to einsum and einops
MCP server that gives AI assistants on-demand access to 1,500+ amCharts docs, ~300 code examples, and 1000+ class API references.
A Model Context Protocol server for generating visual charts using AntV.
Query and analyze your Opik logs, traces, prompts and all other telemtry data from your LLMs in natural language.
Get access to Kaggle's datasets, models, competitions, notebook and benchmarks.
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