MoziChem▌
by sinagilassi
MoziChem offers process design with flash calculations and equation of state models like Soave Redlich Kwong and van der
Provides thermodynamic and chemical engineering calculations through the MoziChem framework, offering equation of state models and flash calculations for process design, optimization, and phase equilibrium analysis.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Chemical engineers designing separation processes
- / Process optimization and phase equilibrium analysis
- / Researchers studying thermodynamic behavior of chemical systems
- / Students learning chemical engineering calculations
capabilities
- / Calculate fugacity for pure gas and liquid components
- / Calculate fugacity for gas mixtures
- / Analyze equation of state roots for components and mixtures
- / Apply multiple equation of state models (Peng-Robinson, Soave-Redlich-Kwong, etc.)
- / Retrieve method reference inputs and equations
- / Perform thermodynamic property predictions
what it does
Performs chemical engineering calculations including equation of state modeling, fugacity calculations, and thermodynamic property predictions using the MoziChem framework.
about
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.
how to install
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.
license
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.
readme
MoziChem-MCP
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.
🧪 Overview
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.
🚀 Features
Current MCP Servers
-
🌡️ EOS Models MCP (
eos-models-mcp)- Equation of State calculations using various models (Peng-Robinson, Soave-Redlich-Kwong, Redlich-Kwong, van der Waals)
- Fugacity calculations for pure components and mixtures
- Thermodynamic property predictions
- Phase behavior analysis
-
⚖️ Flash Calculations MCP (
flash-calculations-mcp)- Vapor-liquid equilibrium calculations
- Multi-component phase equilibrium
- Temperature and pressure flash calculations
- Bubble point and dew point calculations
📦 Installation
Prerequisites
- Python 3.13 or higher
- uv package manager (recommended)
Install from Source
# 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 .
Install from PyPI (when available)
pip install mozichem-mcp
🔧 Usage
Running MCP Servers
Each MCP server can be run independently:
EOS Models MCP Server
# 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
Flash Calculations MCP Server
# 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
Integration with AI Assistants
These MCP servers are designed to work with AI assistants that support the Model Context Protocol, such as:
- Claude Desktop
- Other MCP-compatible AI tools
Example Configuration for Claude Desktop
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"]
}
}
}
Example Calculations
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"
📚 Documentation
Chemical Engineering Applications
- Process Design: Use for preliminary process calculations and design
- Research: Integrate with computational workflows for chemical engineering research
- Education: Enhance learning with interactive thermodynamic calculations
- Industry: Support engineering decisions with reliable thermodynamic data
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request to improve the project.
📝 License
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.
❓ FAQ
For any questions, contact me on LinkedIn.
👨💻 Authors
⭐ Star this repository if you find it useful for your chemical engineering projects!
🐛 Report issues or 💡 suggest new features in the Issues section.
FAQ
- What is the MoziChem MCP server?
- MoziChem is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for MoziChem?
- This profile displays 34 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★34 reviews- ★★★★★James Malhotra· Dec 24, 2024
MoziChem is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sakshi Patil· Nov 23, 2024
Useful MCP listing: MoziChem is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Aisha Abbas· Nov 15, 2024
MoziChem is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Chaitanya Patil· Oct 14, 2024
MoziChem reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Alexander White· Oct 6, 2024
We evaluated MoziChem against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Isabella Chen· Sep 25, 2024
I recommend MoziChem for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★James Yang· Sep 17, 2024
Strong directory entry: MoziChem surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Oshnikdeep· Sep 1, 2024
We wired MoziChem into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ganesh Mohane· Aug 20, 2024
MoziChem is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Lucas Shah· Aug 16, 2024
Strong directory entry: MoziChem surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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