by sinagilassi
MoziChem offers process design with flash calculations and equation of state models like Soave Redlich Kwong and van der
Performs chemical engineering calculations including equation of state modeling, fugacity calculations, and thermodynamic property predictions using the MoziChem framework.
MoziChem is a community-built MCP server published by sinagilassi that provides AI assistants with tools and capabilities via the Model Context Protocol. MoziChem offers process design with flash calculations and equation of state models like Soave Redlich Kwong and van der It is categorized under ai ml. This server exposes 6 tools that AI clients can invoke during conversations and coding sessions.
You can install MoziChem in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
MoziChem is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
MoziChem is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: MoziChem is the kind of server we cite when onboarding engineers to host + tool permissions.
MoziChem is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
MoziChem reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated MoziChem against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend MoziChem for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: MoziChem surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We wired MoziChem into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
MoziChem is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: MoziChem surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A collection of Model Context Protocol (MCP) servers for chemical engineering and chemistry applications, built on top of the powerful MoziChem framework. This repository provides specialized MCP tools that enable AI assistants to perform complex chemical calculations, thermodynamic modeling, and process engineering tasks.
MoziChem-MCP bridges the gap between AI language models and chemical engineering calculations by providing structured access to thermodynamic models, equation of state calculations, phase equilibrium computations, and other essential chemical engineering tools through the Model Context Protocol.
Important Notes: This repository is actively maintained and will be updated with new MCP servers and features in the future. Stay tuned for additions to support more chemical engineering domains.
🌡️ EOS Models MCP (eos-models-mcp)
⚖️ Flash Calculations MCP (flash-calculations-mcp)
# Clone the repository
git clone https://github.com/sinagilassi/mozichem-mcp.git
cd mozichem-mcp
# Install using uv (recommended)
uv sync
# Or install using pip
pip install -e .
pip install mozichem-mcp
Each MCP server can be run independently:
# Using uvx with the published package
uvx --from mozichem-mcp mozichem-mcp-eos-models
# Or run directly with Python (if installed locally)
python -m mozichem_mcp.mcp.eos_models
# Using uvx with the published package
uvx --from mozichem-mcp mozichem-mcp-flash-calculation
# Or run directly with Python (if installed locally)
python -m mozichem_mcp.mcp.flash_calculation
These MCP servers are designed to work with AI assistants that support the Model Context Protocol, such as:
Add to your Claude Desktop configuration:
{
"mcpServers": {
"mozichem-eos": {
"command": "uvx",
"args": ["--from", "mozichem-mcp", "mozichem-mcp-eos-models"]
},
"mozichem-flash": {
"command": "uvx",
"args": ["--from", "mozichem-mcp", "mozichem-mcp-flash-calculation"]
}
}
}
Once integrated with an AI assistant, you can perform calculations like:
"Calculate the fugacity of methane at 300K and 10 bar using the Peng-Robinson equation of state"
"Perform a flash calculation for a mixture of 40% methane and 60% ethane at 250K and 20 bar"
Contributions are welcome! Please feel free to submit a Pull Request to improve the project.
This project is licensed under the MIT License. You are free to use, modify, and distribute this software in your own applications or projects. However, if you choose to use this app in another app or software, please ensure that my name, Sina Gilassi, remains credited as the original author. This includes retaining any references to the original repository or documentation where applicable. By doing so, you help acknowledge the effort and time invested in creating this project.
For any questions, contact me on LinkedIn.
⭐ Star this repository if you find it useful for your chemical engineering projects!
🐛 Report issues or 💡 suggest new features in the Issues section.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.