by axiomatic-ai
Extract plot data from images with Axiomatic AI. Use advanced web plot digitizer features for scientific imaging and ana
Provides six specialized servers for scientific workflows including photonic circuit design with gdsfactory, OCR document processing, plot data extraction, equation analysis, PDF annotation, and model fitting.
Axiomatic AI is a community-built MCP server published by axiomatic-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Extract plot data from images with Axiomatic AI. Use advanced web plot digitizer features for scientific imaging and ana It is categorized under ai ml, analytics data.
You can install Axiomatic AI 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
Axiomatic AI 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
Axiomatic AI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: Axiomatic AI is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Axiomatic AI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Axiomatic AI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: Axiomatic AI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: Axiomatic AI is the kind of server we cite when onboarding engineers to host + tool permissions.
We wired Axiomatic AI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Axiomatic AI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Axiomatic AI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired Axiomatic AI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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MCP (Model Context Protocol) servers that provide AI assistants with access to the Axiomatic_AI Platform - a suite of advanced tools for scientific computing, document processing, and photonic circuit design.
uv tool install "axiomatic-mcp[pic]".You will receive an API key by email shortly after filling the form. Check your spam folder if it doesn't arrive.
claude mcp add axiomatic-mcp --env AXIOMATIC_API_KEY=your-api-key-here -- uvx --from axiomatic-mcp all
{
"mcpServers": {
"axiomatic-mcp": {
"command": "uvx",
"args": ["--from", "axiomatic-mcp", "all"],
"env": {
"AXIOMATIC_API_KEY": "your-api-key-here"
}
}
}
}
Follow the MCP install guide and use the standard configuration above. See the official instructions here: Gemini CLI MCP Server Guide
{
"axiomatic-mcp": {
"command": "uvx",
"args": ["--from", "axiomatic-mcp", "all"],
"env": {
"AXIOMATIC_API_KEY": "your-api-key-here"
}
}
}
Follow the Windsurf MCP documentation. Use the standard configuration above.
{
"axiomatic-mcp": {
"command": "uvx",
"args": ["--from", "axiomatic-mcp", "all"],
"env": {
"AXIOMATIC_API_KEY": "your-api-key-here"
}
}
}
</details> <details> <summary><strong>💻 Codex</strong></summary>Note: After installing via the button, open LM Studio MCP settings and add:
"env": { "AXIOMATIC_API_KEY": "your-api-key-here" }
Create or edit the configuration file ~/.codex/config.toml and add:
[mcp_servers.axiomatic-mcp]
command = "uvx"
args = ["--from", "axiomatic-mcp", "all"]
env = { AXIOMATIC_API_KEY = "your-api-key-here" }
For more information, see the Codex MCP documentation
</details> <details> <summary><strong>🌊 Other MCP Clients</strong></summary>Use this server configuration:
{
"command": "uvx",
"args": ["--from", "axiomatic-mcp", "all"],
"env": {
"AXIOMATIC_API_KEY": "your-api-key-here"
}
}
Note: This installs all tools except for AxPhotonicsPreview under one server. If you experience other issues, try individual servers instead.
Found a bug? Please help us fix it by creating a bug report.
Join our Discord to engage with other engineers and scientists using Axiomatic Operators. Ask for help, discuss bugs and features, and become a part of the Axiomatic community!
It's not recommended to install axiomatic operators inside a conda environment. uv handles seperate python environments so it is safe to run "globally" without affecting your existing Python environments
We have seen reports of the cursor window not opening correctly. If this happens you may manually add to cursor by:
{
"mcpServers": {
"axiomatic-mcp": {
"command": "uvx --from axiomatic-mcp all",
"env": {
"AXIOMATIC_API_KEY": "YOUR API KEY"
},
"args": []
}
}
}
Install only the domain servers you need. Each server runs independently, so you can add/remove them as needed.
If you experience any issues such as tools not appearing, it may be that you are using an old version and need to clear uv's cache to update it.
uv cache clean
Then restart your MCP client (e.g. restart Cursor).
This clears the uv cache and forces fresh downloads of packages on the next run.
You may find more information about each server and how to install them individually in their own READMEs.
Compose equation of your interest based on information in the scientific paper.
Convert PDF documents to markdown with advanced OCR and layout understanding.
Create intelligent annotations for PDF documents with contextual analysis, equation extraction, and parameter identification.
Design photonic integrated circuits using natural language descriptions. Additional requirements are needed, please refer to Check system requirements
Extract numerical data from plot images for analysis and reproduction.
Fit parametric models or digital twins to observational data using advanced statistical analysis and optimization algorithms.
Have an idea for a new feature? We'd love to hear it! Submit a feature request and:
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.