CCM
/MCP
SkillsMCPMarketplacesDigestToolsAdvertise

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by Category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP Servers by Category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by Category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control

Claude Code Marketplaces

Discover Claude Code plugins, extensions, and tools. Automatically updated directory of Anthropic Claude AI marketplaces with development tools, productivity plugins, and integrations.

Resources

  • Browse Skills
  • Browse MCP Servers
  • Browse Marketplaces
  • Skill index
  • MCP index
  • Marketplace index
  • Plugins Reference

Community

  • About
  • Tools
  • Feedback
  • Privacy Policy
  • Advertise

Built for the Claude Code community with Claude Code by mertbuilds.com

Independent project, not affiliated with Anthropic
jonesrobm avatar

Physbound

jonesrobm/physbound
1STDIOregistry active
Summary

Validates RF calculations against hard physical limits to catch AI hallucinations in engineering workflows. Exposes four tools: rf_link_budget for Friis transmission equation checks with aperture limit validation, shannon_hartley for channel capacity verification, noise_floor for thermal noise and cascaded noise figure calculations, and radar_range for monostatic detection analysis. Returns structured errors with LaTeX explanations when claims violate Shannon limits, thermal noise floors, or antenna gain bounds. Reach for this when reviewing link budgets, vetting telecom proposals, or designing receiver chains where impossible numbers could slip through. Built on SciPy's CODATA constants with dimensional analysis through Pint. The repo includes a full hallucination test suite showing where LLMs commonly fail on SNR, aperture, and fourth-root radar calculations.

CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
ego lite browserego lite browser
ego lite browser
Fastest browser for AI agents to run web automation tasks, always free.
Download Free life-time →
Give your AI the whole web as clean markdownGive your AI the whole web as clean markdown
Give your AI the whole web as clean markdown
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
belt - the only tool your agent needs
belt - the only tool your agent needs
belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Agent, connect blockchain
Agent, connect blockchain
Connect your Claude agent to live crypto prices and trading routes via 1inch
Get the MCP →
inference shell
inference shell
create and run specialised agents in minutes
build now →
CodeHealth MCP ServerCodeHealth MCP Server
CodeHealth MCP Server
Protect your code quality, stop the AI slop.
Try For Free →
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
ego lite browserego lite browser
ego lite browser
Fastest browser for AI agents to run web automation tasks, always free.
Download Free life-time →
Give your AI the whole web as clean markdownGive your AI the whole web as clean markdown
Give your AI the whole web as clean markdown
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
belt - the only tool your agent needs
belt - the only tool your agent needs
belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Agent, connect blockchain
Agent, connect blockchain
Connect your Claude agent to live crypto prices and trading routes via 1inch
Get the MCP →
inference shell
inference shell
create and run specialised agents in minutes
build now →
CodeHealth MCP ServerCodeHealth MCP Server
CodeHealth MCP Server
Protect your code quality, stop the AI slop.
Try For Free →

PhysBound

PhysBound

Physical Layer Linter — an MCP server that validates RF and physics calculations against hard physical limits, catching AI hallucinations in engineering workflows.

CI codecov PyPI MCP Registry License: MIT Python 3.12+ MCP Badge Ko-fi


PhysBound CLI catching a Shannon-Hartley violation

LLMs generate plausible-sounding RF numbers that violate fundamental physics — throughput above the Shannon limit, antenna gains no aperture can produce, radar ranges the range equation forbids. PhysBound gives any MCP-compatible AI assistant six validated calculation tools, backed by CODATA constants (via SciPy) and dimensional analysis (via Pint). Impossible claims return structured PhysicalViolationError responses with the violated law, the computed limit, and a LaTeX explanation — not silent failures.

ToolWhat it validates
rf_link_budgetFriis link budgets: FSPL, received power, antenna gains vs. aperture/Harrington limits
shannon_hartleyThroughput claims against channel capacity C = B log2(1 + SNR)
noise_floorThermal noise kTB, Friis noise-figure cascades, receiver sensitivity
radar_rangeDetection-range claims against the monostatic radar range equation
antenna_gainGain limits, beamwidth, and far-field distance for a single antenna
radar_ambiguityPulse-Doppler unambiguous range/velocity, Doppler aliasing, range resolution

Installation

PhysBound is a standard stdio MCP server published on PyPI. The recommended launch command is uvx physbound, which fetches and runs the latest release in an isolated environment — no manual install step.

First run: uvx downloads ~60 MB of dependencies (SciPy, NumPy) the first time. Run uvx physbound once in a terminal to pre-cache them (Ctrl-C to exit); subsequent starts are instant.

Claude Code

claude mcp add physbound -- uvx physbound

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "physbound": {
      "command": "uvx",
      "args": ["physbound"]
    }
  }
}

Cursor, Windsurf, and other MCP clients

Use the same JSON server entry as above in your client's MCP configuration file (Cursor: ~/.cursor/mcp.json; Windsurf: ~/.codeium/windsurf/mcp_config.json).

Without uv

If you prefer a plain Python install (requires Python 3.12+):

pip install physbound

then set "command": "physbound" (with no args) in the client configuration.

Once configured, ask your assistant an RF question — "Can a 20 MHz channel with 15 dB SNR support 500 Mbps?" — and it will answer with physics-validated numbers.

Command-Line Usage

The same validators are available directly from the terminal — useful in scripts and CI pipelines, since a physics violation exits with code 1:

$ physbound check shannon --bandwidth-hz 20e6 --snr-db 15 --claimed-throughput-bps 500e6
PHYSICS VIOLATION [Shannon-Hartley Theorem]
  Claimed throughput 500000000.0 bps exceeds Shannon limit of 100556153.5 bps by 397.2%
  Computed limit: 1.00556e+08 bps
  Claimed value:  5e+08 bps
$ echo $?
1
$ physbound check shannon --bandwidth-hz 20e6 --snr-db 15 --claimed-throughput-bps 100e6
Shannon-Hartley Capacity: ...   # exit code 0

Available subcommands: check {link-budget, shannon, noise, radar-range, antenna, radar-ambiguity} (flags mirror the MCP tool parameters; add --json for the full structured result) and serve --transport {stdio,http} for running the MCP server over HTTP. See physbound check --help for details.

What LLMs Get Wrong

Sixteen real hallucination patterns, each caught by PhysBound's test suite:

#CategoryLLM HallucinationPhysBound TruthVerdict
1Shannon-HartleyA 20 MHz 802.11n channel with 15 dB SNR can achieve 500 MbpsShannon limit: 100.6 Mbps (not 500 Mbps)CAUGHT
2Shannon-HartleyA 100 MHz 5G channel with 20 dB SNR delivers 2 GbpsShannon limit: 665.8 Mbps (not 2000 Mbps)CAUGHT
3Antenna ApertureA 30 cm dish antenna at 1 GHz provides 45 dBi gainPhysical limit: 12.1 dBi (Harrington); aperture eta=1: 9.9 dBi; typical dish: 7.4 dBi (eta=0.55) (not 45 dBi)CAUGHT
4Thermal NoiseReceiver noise floor of -180 dBm/Hz at room temperatureThermal noise floor: -174.0 dBm/Hz at 290K (not -180 dBm/Hz)CAUGHT
5Link Budget / FSPLWi-Fi at 2.4 GHz with 20 dBm TX reaches 10 km with -40 dBm RX powerActual RX power at 10 km: -94.1 dBm (not -40 dBm)CAUGHT
6Link Budget / FSPLA 1W transmitter at 12 GHz with 0 dBi antennas reaches GEO at -80 dBmActual RX power at GEO: -175.1 dBm (not -80 dBm)CAUGHT
7Link Budget / FSPLBluetooth at 2.4 GHz with 0 dBm TX and 0 dBi antennas reaches 1 km at -60 dBmActual RX power at 1 km: -100.1 dBm (not -60 dBm)CAUGHT
8Shannon-HartleyA 10 MHz LTE channel at 10 dB SNR supports 1 GbpsShannon limit: 34.6 Mbps (not 1000 Mbps)CAUGHT
9Noise CascadeReceiver NF is the same regardless of stage order: LNA(20dB/1.5dB) + Mixer(10dB/8dB)LNA first: 1.66 dB vs mixer first: 8.03 dB (penalty: 6.4 dB)CAUGHT
10Antenna ApertureA 10 cm patch antenna at 900 MHz provides 20 dBi gainPhysical limit: 4.4 dBi (Harrington, D < lambda); aperture eta=1: -0.5 dBi; typical: -3.1 dBi (eta=0.55) (not 20 dBi)CAUGHT
11Radar Range EquationDoubling transmit power doubles radar detection rangeRange increases by factor 1.189 (2^(1/4) = 1.189), not 2.0CAUGHT
12Radar Range EquationSmall drone (0.01 m^2 RCS) detectable at 200 km by 1 kW X-band radar with 30 dBi gainMax range: 2.7 km for 0.01 m^2 RCS at 1 kW X-band (not 200 km)CAUGHT
13Antenna GainA 0.5 m user-terminal dish at 12 GHz gives 50 dBi gainPhysical limit: 36.1 dBi (Harrington); typical dish: 33.4 dBi (eta=0.55) (not 50 dBi)CAUGHT
14Antenna GainA 3 m dish at 10 GHz is in its far field at 10 m, so gain can be measured thereFar-field distance 2D^2/lambda = 600 m; 10 m is in the near fieldCAUGHT
15Radar AmbiguityA 10 GHz radar at 10 kHz PRF unambiguously measures 500 m/sv_ua = lambda*PRF/4 = +/-74.9 m/s (blind speed 149.9 m/s); 500 m/s aliases (not 500 m/s)CAUGHT
16Radar AmbiguityA 10 GHz pulse-Doppler radar can unambiguously cover 150 km and +/-300 m/s at onceR_uav_ua = clambda/8 = 1.123e+06 m^2/s for any PRF; claimed 4.5e+07 m^2/s (40x too large)CAUGHT

Generated automatically by pytest tests/test_marketing.py -s

Tools

Full derivations, sources, and worked examples for every formula are in docs/formulas.md.

rf_link_budget

Computes a complete RF link budget using the Friis transmission equation and validates antenna gains against physical limits.

Example: "What's the received power for a 2.4 GHz link at 100 m with 20 dBm TX, 10 dBi TX gain, 3 dBi RX gain?"

Returns FSPL, received power, wavelength, and — when antenna diameters are supplied — gain limit checks. Gains above the hard bound G_max = max((pi D / lambda)^2, (ka)^2 + 2ka) (the eta = 1 aperture value, or Harrington's bound for electrically small antennas) are rejected; gains above the typical dish value (eta = 0.55) are accepted with a warning. Also warns inside the far-field distance 2D^2/lambda and rejects negative losses.

shannon_hartley

Computes Shannon-Hartley channel capacity C = B log2(1 + SNR) and validates throughput claims.

Example: "Can a 20 MHz channel with 15 dB SNR support 500 Mbps?"

Returns theoretical capacity, spectral efficiency, and whether the claim is physically possible, including the exact percentage by which a violating claim exceeds the Shannon limit.

noise_floor

Computes thermal noise power N = k_B T B, cascades noise figures through multi-stage receivers with the Friis noise formula, and calculates receiver sensitivity.

Example: "What's the noise floor for a 1 MHz receiver at 290 K with a two-stage LNA chain?"

Returns thermal noise in dBm and watts, cascaded noise figure, effective input noise temperature T_e = 290 K * (F - 1), and receiver sensitivity.

radar_range

Computes the monostatic radar range equation R_max = [P_t G^2 lambda^2 sigma / ((4pi)^3 S_min L)]^(1/4) and validates detection-range claims.

Example: "Can a 1 kW X-band radar with 30 dBi gain detect a 0.01 m^2 drone at 200 km?"

Returns maximum detection range, minimum detectable signal, wavelength, and intermediate values. Catches the common fourth-root fallacy that doubling power doubles range.

antenna_gain

Analyses a single antenna from its diameter or physical area: gain limits, beamwidth, and far-field distance, with optional validation of a claimed gain.

Example: "Can a 0.5 m dish at 12 GHz really give 50 dBi? What beamwidth should I expect?"

Returns the physical gain limit (aperture or Harrington bound, and which one governs), typical gain at the given efficiency (default 0.55), effective aperture, half-power beamwidth estimates, far-field distance 2D^2/lambda, and — for a claimed gain — the implied aperture efficiency and validity.

radar_ambiguity

Computes pulse-Doppler ambiguity limits for a given carrier frequency and PRF.

Example: "Can a 10 GHz radar at 10 kHz PRF unambiguously measure a 500 m/s target out to 150 km?"

Returns unambiguous range R_ua = c / (2 PRF), unambiguous velocity v_ua = lambda PRF / 4, blind speed, Doppler shift and apparent (aliased) velocity, range resolution (c tau / 2, or c / 2B with pulse compression), minimum range, duty cycle, and the range-Doppler dilemma invariant R_ua v_ua = c lambda / 8. Rejects range, velocity, or resolution claims the PRF or pulse cannot support.

Physics Guarantees

Every calculation is validated against hard physical limits:

  • Speed of light: c = 299,792,458 m/s — no exceptions
  • Thermal noise floor: N = -174 dBm/Hz at 290 K — the IEEE standard reference
  • Shannon limit: C = B log2(1 + SNR) — no throughput claim exceeds this
  • Antenna gain limit: G_max = max((pi D / lambda)^2, (ka)^2 + 2ka) — the eta = 1 aperture value for planar apertures and Harrington's bound for any antenna in a sphere of diameter D; eta = 0.55 is a warning threshold, not a limit
  • Radar range equation: R_max = [P_t G^2 lambda^2 sigma / ((4pi)^3 S_min)]^(1/4) — range obeys the fourth-root law
  • Receiver sensitivity: S_min = k (T_A + T_0 (F - 1)) B * SNR with T_e = T_0 (F - 1) referenced to 290 K
  • Unambiguous range: R_ua = c / (2 PRF) — echoes beyond it fold into a later pulse interval
  • Unambiguous velocity: v_ua = lambda PRF / 4 — Doppler is sampled at the PRF, so |f_d| <= PRF/2
  • Range-Doppler dilemma: R_ua * v_ua = c lambda / 8 — no PRF choice beats it for a given carrier

Violations return structured PhysicalViolationError responses with LaTeX explanations, not silent failures.

Examples and Documentation

  • Catching Hallucinations — walkthrough of five real LLM failure modes with full JSON responses
  • Interactive Demo Notebook — Jupyter notebook calling the physics engines directly
  • Formula Reference — every formula, constant, and validation guard with sources
  • Changelog — release history

Development

Requires uv and Python 3.12+ (CI covers 3.12–3.14):

# Clone and install
git clone https://github.com/JonesRobM/physbound.git
cd physbound
uv sync --all-extras

# Run checks
uv run pytest tests/ -v
uv run ruff check src/ tests/
uv run mypy src/physbound/

# Regenerate the hallucination table above
uv run pytest tests/test_marketing.py -s

# Start the MCP server locally
uv run physbound

Contributions that expand the set of validated physics domains are welcome — see CONTRIBUTING.md for the architecture guide and the step-by-step recipe for adding a new validator.

Use Cases

  • RF system design review — validate link budgets, receiver sensitivity, and noise cascades
  • Telecom proposal vetting — catch impossible throughput claims before they reach a customer
  • Radar system sizing — sanity-check detection range, PRF selection, and ambiguity trade-offs
  • 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

Support

If PhysBound is useful in your work, consider buying me a coffee.

License

MIT License. See LICENSE.

Related

  • Model Context Protocol — the open standard for AI tool integration
  • MCP Server Registry — official directory of MCP servers
  • FastMCP — Python framework for building MCP servers
Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
ego lite browserego lite browser
ego lite browser
Fastest browser for AI agents to run web automation tasks, always free.
Download Free life-time →
Give your AI the whole web as clean markdownGive your AI the whole web as clean markdown
Give your AI the whole web as clean markdown
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
belt - the only tool your agent needs
belt - the only tool your agent needs
belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Agent, connect blockchain
Agent, connect blockchain
Connect your Claude agent to live crypto prices and trading routes via 1inch
Get the MCP →
inference shell
inference shell
create and run specialised agents in minutes
build now →
CodeHealth MCP ServerCodeHealth MCP Server
CodeHealth MCP Server
Protect your code quality, stop the AI slop.
Try For Free →
Categories
Developer Tools
Registryactive
Packagephysbound
TransportSTDIO
UpdatedFeb 8, 2026
View on GitHub

Related Developer Tools MCP Servers

View all →
kascada avatar
Logmcp

kascada/logmcp

MCP server — read-only log access for AI assistants, without giving AI shell access.
1
kitepon-rgb avatar
Aiterm Mcp

kitepon-rgb/aiterm-mcp

AI drives one persistent tmux-backed terminal over MCP; SSH and containers nest inside it.
1
mrorigo avatar
Bash Command Mcp

mrorigo/bash-command-mcp

Sophisticated bash command MCP server that runs and manages shell execution.
1
sandip124 avatar
Wisegit

sandip124/wisegit

Extracts decision intent from git history and protects intentional code from AI modification.
1
sean-sunagaku avatar
Promptlint MCP

sean-sunagaku/promptlint-mcp

Static linter for AI prompts: contradictions, redundancy, ambiguity, and fluff.
1
shaqmughal avatar
Seekstone

shaqmughal/seekstone

Filesystem-direct Obsidian MCP server — search and edit your vault with low context-tax.
1