Last updated
March 16, 2026
MCP profile
Github JonesRobM Physbound
Physical Layer Linter — validates RF link budgets, Shannon capacity, and noise floors.
Developer ToolsPackagePythonOpen SourceExternal
Visibility
Public
ByRegistry
About This MCP Server
Physical Layer Linter — validates RF link budgets, Shannon capacity, and noise floors. This MCP server enables AI assistants like Claude to seamlessly interact with Github JonesRobM Physbound, providing structured access to its functionality through the Model Context Protocol (MCP).
Key Capabilities:
- Manage repositories and version control operations
- Review and analyze code changes
- Automate code review and quality checks
- Track issues and project management
Common Use Cases:
- AI-assisted code review and analysis
- Automating repository management tasks
- Streamlining development workflows
- Tracking and managing project issues
How It Works: Github JonesRobM Physbound integrates with AI coding assistants and chat interfaces through the standardized MCP protocol. Once configured, your AI assistant can directly invoke Github JonesRobM Physbound's tools, enabling natural language interaction with its features without manual API calls or custom integrations.
Technical Details: Server type: Package · Language: Python
Capabilities
AI coding assistants are increasingly used in RF engineering, telecommunications, and signal processing workflows. But LLMs have no intrinsic understanding of physics. They generate plausible-sounding numbers that can violate fundamental laws like Shannon-Hartley, thermodynamic noise limits, and antenna aperture bounds.PhysBound acts as a physics guardrail for any MCP-compatible AI assistant. Every calculation is checked against CODATA physical constants via SciPy, with dimensional analysis enforced through Pint. Violations return structured errors with LaTeX explanations, not silent failures.
Tools & Endpoints
Example Workflow
• Catching Hallucinations — walkthrough of four real LLM failure modes with full JSON responses
• Interactive Demo Notebook — hands-on Jupyter notebook calling the physics engines directly
What Problems It Solves
- RF system design review — validate link budgets, receiver sensitivity, and noise cascades
- Telecom proposal vetting — catch impossible throughput claims before they reach a customer
- Educational tools — teach Shannon-Hartley, Friis transmission, and thermal noise with verified calculations
- CI/CD for physics — integrate as a validation step in engineering pipelines
Why Use Github JonesRobM Physbound?
- AI coding assistants are increasingly used in RF engineering, telecommunications, and signal processing workflows. But LLMs have no intrinsic understanding of physics. They generate plausible-sounding numbers that can violate fundamental laws like Shannon-Hartley, thermodynamic noise limits, and antenna aperture bounds.
- PhysBound acts as a physics guardrail for any MCP-compatible AI assistant. Every calculation is checked against CODATA physical constants via SciPy, with dimensional analysis enforced through Pint. Violations return structured errors with LaTeX explanations, not silent failures.
Specifications
Status
live
Industry
Developer Tools
Category
General
Server type
Package
Language
Python
License
Open Source
Verified
Yes
Hosting
Hosting Options
- Package
API
Integrate this server into your application. Choose a connection method below.
1
Install
Install command
Python
pip install physbound2
Configure
Configuration
json
{
"mcpServers": {
"physbound": {
"command": "uvx",
"args": ["physbound"]
}
}
}Performance
Usage
Quick Reference
- Name
- Github JonesRobM Physbound
- Function
- Physical Layer Linter — validates RF link budgets, Shannon capacity, and noise floors.
- Transport
- Package
- Language
- Python
- Install
- pip install physbound
- Source
- External (Registry)
- License
- Open Source
Get started
Ready to integrate this MCP server?
Book a demo to see how this server fits your workflow, or explore the full catalog.