chuk-mcp-physics 0.2.4

MCP server for physics simulations and calculations using Rapier physics engine

Physics MCP Server

MCP server for physics simulations and calculations using Rapier physics engine

Provides LLMs with physics superpowers: ballistic calculations, collision prediction, rigid-body simulations, and trajectory recording for 3D visualization.

🚀 Quick Start (30 seconds)

# Try it instantly with uvx (no installation needed)
uvx chuk-mcp-physics

# Or with the public Rapier service for simulations
RAPIER_SERVICE_URL=https://rapier.chukai.io uvx chuk-mcp-physics

# Or use the public hosted MCP server (no local installation)
# Add to Claude Desktop config with URL: https://physics.chukai.io/mcp

For Claude Desktop: Add to your config file:

Option 1: Public Hosted MCP Server (Easiest - No Installation)

{
  "mcpServers": {
    "physics": {
      "command": "node",
      "args": ["-e", "require('https').get('https://physics.chukai.io/mcp')"]
    }
  }
}

Option 2: Local uvx (Recommended)

{
  "mcpServers": {
    "physics": {
      "command": "uvx",
      "args": ["chuk-mcp-physics"],
      "env": {
        "RAPIER_SERVICE_URL": "https://rapier.chukai.io"
      }
    }
  }
}

---

🎯 Use Cases

1. Interactive Physics Education

LLM as Physics Tutor

User: "If I throw a ball at 20 m/s at 45°, how far will it go?"
LLM: [calls calculate_projectile_motion]
     "The ball will travel 40.8 meters and reach a maximum height of 10.2 meters..."
     [generates visualization with trajectory points]

Real-World Problem Solving

• Students ask physics questions in natural language
• LLM calculates exact answers using the MCP tools
• Can generate trajectory plots, force diagrams, energy graphs
• Interactive "what-if" scenarios: "What if gravity was half?"

---

2. Game Development & Prototyping

Ballistics Design

User: "I'm designing a cannon in my game. Initial velocity 50 m/s, 30° angle.
       Will it clear a 15m wall at 80m distance?"
LLM: [calls calculate_projectile_motion]
     "At 80m, the projectile is at 18.6m height - it WILL clear the wall.
      It lands at 110.9m range."

Collision Detection

User: "Two spaceships: Ship A at (0,0,0) moving at (10,0,0) m/s, Ship B at (100,5,0)
       moving at (-8,0,0) m/s. Will they collide?"
LLM: [calls check_collision]
     "Yes, collision in 5.3 seconds at position (53.0, 2.65, 0.0) with impact speed 18 m/s"

Rapid Iteration

• Test different launch angles, speeds, masses without coding
• Verify collision logic before implementation
• Generate realistic physics data for procedural content

---

3. 3D Visualization & Animation (React Three Fiber)

Automated Animation Data Generation

User: "Create a realistic basketball shot animation - 7m throw into 3m high basket"
LLM: [calls calculate_projectile_motion with solved angle]
     [returns trajectory_points array]
     "Here's the trajectory data for R3F. The ball needs 52° launch angle..."

     <mesh position={interpolate(trajectory)}>
       <sphereGeometry args={[0.12]} />
     </mesh>

Rigid-Body Simulations

User: "Simulate 10 boxes falling and stacking in a pile"
LLM: [calls create_simulation]
     [calls add_rigid_body for ground + 10 boxes with random positions]
     [calls record_trajectory for each box]
     "Here are 10 trajectory arrays for your R3F scene, ready to use..."

Use Cases:

• Product visualizations (dropping phones, bouncing balls)
• Architectural collapse simulations (building demolition previews)
• Sports analytics visualizations (ball trajectories, collision analysis)
• Sci-fi effects (asteroid fields, debris clouds)

---

4. Engineering & Design Analysis

Vehicle Crash Prediction

User: "Two cars: Car A (1500kg) at 30 m/s, Car B (1200kg) at 25 m/s approaching.
       Distance 100m. When do they collide and what's the impact energy?"
LLM: [calls check_collision for timing]
     [calls calculate_kinetic_energy for both cars]
     [calls calculate_momentum for momentum analysis]
     "Collision in 1.82 seconds. Total kinetic energy: 1,050,000 J..."

Safety Analysis

• Calculate impact forces for crash test scenarios
• Predict collision times for autonomous vehicle planning
• Analyze momentum transfer in industrial equipment

Structural Testing (with Rapier simulations)

• Simulate falling objects hitting structures
• Test load-bearing capacity under dynamic loads
• Model chain reactions (domino effects)

---

5. Sports & Athletics

Performance Optimization

User: "What's the optimal angle to throw a javelin at 25 m/s for maximum distance?"
LLM: [calls calculate_projectile_motion at multiple angles]
     "45° gives maximum range of 63.7 meters on flat ground.
      However, with 2m release height, 43° is optimal at 64.9m..."

Shot Analysis

• Basketball: arc optimization, free throw angles
• Golf: ball trajectory, wind effects (with manual adjustments)
• Baseball: pitch speed vs. distance calculations
• Track & field: javelin, shot put, long jump optimization

---

6. Astrophysics & Space Exploration

Orbital Mechanics (Simplified)

User: "Two asteroids on collision course. A: 500m radius at (0,0,0) moving 15 km/s.
       B: 300m radius at (100km, 2km, 0) moving -12 km/s. Impact prediction?"
LLM: [calls check_collision with appropriate units]
     "Collision in 3.47 seconds at closest approach distance 650m.
      They will NOT collide - miss distance is 150m..."

Applications:

• Asteroid impact prediction
• Satellite collision avoidance
• Debris field analysis
• Launch trajectory planning (simplified cases)

---

7. Film & VFX Pre-visualization

Stunt Planning

User: "Car jumps off 3m ramp at 25 m/s, 20° angle. How far does it fly and where does it land?"
LLM: [calls calculate_projectile_motion]
     "Airtime: 1.75 seconds, landing at 42.9m horizontal distance,
      impact speed 26.3 m/s. Recommend crashmat at 40-45m mark..."

Destruction Sequences

• Building collapses with rigid-body sim
• Explosion debris trajectories
• Vehicle stunts and crashes
• Realistic object interactions

---

8. Military & Defense (Training/Education)

Ballistics Training

User: "Artillery shell: muzzle velocity 800 m/s, 45° elevation. Range and time of flight?"
LLM: [calls calculate_projectile_motion]
     "Range: 65.3 km, flight time: 115.5 seconds, max altitude: 16.3 km"

Collision Avoidance

• Projectile trajectory analysis
• Impact point prediction
• Intercept course calculations

---

9. Robotics & Automation

Path Planning

User: "Robot arm needs to toss part into bin 2m away, 0.5m higher.
       What velocity is needed?"
LLM: [reverse-calculates using projectile_motion multiple times]
     "Minimum velocity: 4.7 m/s at 38° angle. Recommend 5.0 m/s for safety margin..."

Collision Detection

• Multi-robot coordination
• Object catching/throwing
• Assembly line optimization

---

10. Data Science & Research

Physics Simulations for ML Training Data

# Generate thousands of collision scenarios for ML model training
for i in range(10000):
    result = await check_collision(random_params())
    training_data.append({
        'features': params,
        'label': result.will_collide,
        'impact_time': result.collision_time
    })

Use Cases:

• Generate labeled physics data for ML models
• Validate physics-informed neural networks
• Test scientific hypotheses with rapid iteration
• Monte Carlo simulations (vary parameters, aggregate results)

---

🚀 Real-World Example Workflows

Workflow 1: Basketball Shot Optimizer

1. User: "I'm 2m tall shooting from free-throw line (4.6m). Basket is 3.05m high.
          What's the minimum velocity needed?"

2. LLM calls calculate_projectile_motion with varying velocities
   - Try v=5 m/s → doesn't reach
   - Try v=7 m/s → reaches
   - Binary search finds minimum: v=6.2 m/s at 52° angle

3. LLM: "Minimum velocity is 6.2 m/s at 52° launch angle.
         For comfortable margin, use 7.0 m/s (typical free throw speed).
         Here's the trajectory visualization..."

Workflow 2: Car Crash Investigation

1. User: "Analyze accident: Car A (1500kg) skid marks 30m, Car B (1200kg) skid marks 25m.
          Coefficient of friction 0.7. What were impact speeds?"

2. LLM:
   - Calculates deceleration from friction: a = μg = 0.7 × 9.81 = 6.87 m/s²
   - Uses v² = 2ad to find velocities
   - Calls calculate_kinetic_energy for both cars
   - Calls calculate_momentum for momentum analysis

3. LLM: "Car A impact speed: ~20.3 m/s (45 mph), Car B: ~18.5 m/s (41 mph).
         Total kinetic energy at impact: 513,000 J. Here's the force analysis..."

Workflow 3: Game Level Design with Physics Simulation

1. User: "Create a Rube Goldberg machine: ball rolls down ramp, hits dominos,
          dominos knock ball into basket"

2. LLM:
   - Calls create_simulation(gravity_y=-9.81)
   - Adds ground plane (static)
   - Adds ramp (static, angled)
   - Adds ball (dynamic, sphere, position at ramp top)
   - Adds 10 dominos (dynamic boxes in a line)
   - Adds basket (static)
   - Calls step_simulation(steps=1000)
   - Analyzes contacts to verify chain reaction
   - Calls record_trajectory for each piece

3. LLM: "Simulation complete! Ball triggers all dominos successfully.
         Here are the trajectories for R3F visualization.
         Domino #3 falls at t=1.2s, domino #7 at t=2.1s..."

Workflow 4: Satellite Collision Warning

1. User: "Satellite A: position (6700km, 0, 0), velocity (0, 7.5km/s, 0)
          Satellite B: position (6650km, 50km, 0), velocity (0, 7.6km/s, 0.1km/s)
          Collision risk?"

2. LLM:
   - Calls check_collision with satellite data
   - Analyzes closest approach

3. LLM: "No collision. Closest approach: 48.3 km at t=412 seconds.
         Satellites are in similar orbits but safe separation.
         Recommend: monitor as orbits may precess over time."

---

🎨 Visualization Integration

React Three Fiber (R3F) Example

function PhysicsAnimation() {
  const [trajectory, setTrajectory] = useState([]);

  useEffect(() => {
    // LLM generated this trajectory data via MCP
    fetch('/api/mcp/record_trajectory', {
      body: JSON.stringify({
        sim_id: "sim_xyz",
        body_id: "ball",
        steps: 300
      })
    }).then(res => setTrajectory(res.frames));
  }, []);

  return (
    <Canvas>
      <AnimatedBall trajectory={trajectory} />
      <Ground />
    </Canvas>
  );
}

function AnimatedBall({ trajectory }) {
  const ref = useRef();

  useFrame((state) => {
    const t = state.clock.getElapsedTime();
    const frame = trajectory[Math.floor(t / 0.016) % trajectory.length];
    if (frame && ref.current) {
      ref.current.position.fromArray(frame.position);
      ref.current.quaternion.fromArray(frame.orientation);
    }
  });

  return (
    <mesh ref={ref}>
      <sphereGeometry args={[0.5]} />
      <meshStandardMaterial color="orange" />
    </mesh>
  );
}

---

📊 Trajectory Data Format

All trajectory recordings follow a canonical schema for maximum interoperability with R3F, Remotion, Three.js, and other animation systems.

Schema Definition

interface Trajectory {
  dt: number;              // Time step between frames (seconds)
  frames: Frame[];         // Ordered list of frames
  meta: {
    body_id: string;       // Fully qualified: "rapier://sim-123/body-1"
    total_time: number;    // Total duration (seconds)
    num_frames: number;    // Frame count
  };
}

interface Frame {
  t: number;                               // Absolute time (seconds)
  position: [number, number, number];      // [x, y, z] in meters
  rotation: [number, number, number, number];  // Quaternion [x, y, z, w]
  velocity?: [number, number, number];     // Optional: linear velocity (m/s)
  angular_velocity?: [number, number, number];  // Optional: angular velocity (rad/s)
}

JSON Example

{
  "dt": 0.016,
  "frames": [
    {
      "t": 0.0,
      "position": [0, 1, 0],
      "rotation": [0, 0, 0, 1],
      "velocity": [0, 0, 0]
    },
    {
      "t": 0.016,
      "position": [0.1, 1.01, 0],
      "rotation": [0, 0.01, 0, 0.9999],
      "velocity": [6.25, 0.61, 0]
    }
  ],
  "meta": {
    "body_id": "rapier://sim-abc123/ball",
    "total_time": 4.8,
    "num_frames": 300
  }
}

Usage in React Three Fiber

import { useRef } from "react";
import { useFrame } from "@react-three/fiber";

function AnimatedObject({ trajectory }) {
  const ref = useRef();

  useFrame((state) => {
    const elapsed = state.clock.getElapsedTime();
    const frameIdx = Math.floor(elapsed / trajectory.dt);
    const frame = trajectory.frames[frameIdx % trajectory.frames.length];

    if (frame && ref.current) {
      ref.current.position.fromArray(frame.position);
      ref.current.quaternion.fromArray(frame.rotation);
    }
  });

  return (
    <mesh ref={ref}>
      <sphereGeometry args={[0.5]} />
      <meshStandardMaterial color="orange" />
    </mesh>
  );
}

Usage in Remotion

import { useCurrentFrame } from "remotion";
import { ThreeCanvas } from "@remotion/three";

export const PhysicsAnimation = ({ trajectory }) => {
  const frame = useCurrentFrame();
  const frameData = trajectory.frames[frame];

  return (
    <AbsoluteFill>
      <ThreeCanvas>
        <mesh position={frameData.position}>
          <sphereGeometry />
        </mesh>
      </ThreeCanvas>
    </AbsoluteFill>
  );
};

Design Notes

• Quaternions for rotation: More compact and interpolation-friendly than Euler angles
• Absolute time: Each frame has absolute time t, making scrubbing easier
• Constant dt: Frames are evenly spaced, simplifying playback
• Optional velocities: Include if needed for motion blur or physics visualization
• Qualified body_id: Format is rapier://sim-{id}/{body_id} for traceability

---

🛠️ Installation

Prerequisites

• Python 3.11+
• For simulations: Rapier service (see RAPIER_SERVICE.md)
  • Public service available at: https://rapier.chukai.io
  • Or run locally with Docker (see below)

Quick Start with uvx (Recommended)

The fastest way to try chuk-mcp-physics without installation:

# Run directly with uvx (no installation needed)
uvx chuk-mcp-physics

# With environment variables
uvx --with chuk-mcp-physics chuk-mcp-physics

Installation Methods

Option 1: Install from PyPI (Recommended)

# Install globally
pip install chuk-mcp-physics

# Or with pipx (isolated environment)
pipx install chuk-mcp-physics

# Run the server
chuk-mcp-physics

# Or via python module
python -m chuk_mcp_physics.server

Option 2: Install from Source

# Clone repository
git clone https://github.com/yourusername/chuk-mcp-physics
cd chuk-mcp-physics

# Install in development mode
make dev-install

# Run the server
chuk-mcp-physics

With Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

Option 1: Using uvx (Recommended - No Installation Required)

{
  "mcpServers": {
    "physics": {
      "command": "uvx",
      "args": ["chuk-mcp-physics"],
      "env": {
        "PHYSICS_PROVIDER": "rapier",
        "RAPIER_SERVICE_URL": "https://rapier.chukai.io"
      }
    }
  }
}

Option 2: Using Installed Package

{
  "mcpServers": {
    "physics": {
      "command": "python",
      "args": ["-m", "chuk_mcp_physics.server"],
      "env": {
        "PHYSICS_PROVIDER": "rapier",
        "RAPIER_SERVICE_URL": "https://rapier.chukai.io"
      }
    }
  }
}

Option 3: Analytic Only (No External Service)

{
  "mcpServers": {
    "physics": {
      "command": "uvx",
      "args": ["chuk-mcp-physics"],
      "env": {
        "PHYSICS_PROVIDER": "analytic"
      }
    }
  }
}

---

📖 Available Tools

Tool Organization

Tools are organized into two tiers to help you choose the right abstraction:

1️⃣ Analytic Primitives (No External Service)

Best for: Quick calculations, education, simple scenarios

Direct formula-based calculations that return instant results:

Tool	What It Does	Example Use Case
calculate_projectile_motion	Ballistic trajectory using kinematic equations	"How far does a cannonball go?"
check_collision	Predict if two spheres will collide	"Will these asteroids hit?"
calculate_force	F = ma calculations	"What force accelerates this car?"
calculate_kinetic_energy	KE = ½mv²	"How much energy in this crash?"
calculate_momentum	p = mv	"What's the momentum transfer?"

Characteristics:

• ⚡ Instant execution (< 1ms)
• 📦 No external dependencies
• 🎯 Exact mathematical solutions
• ✅ Always available (no service required)

Limitations:

• Only spherical objects (for collisions)
• No complex shapes
• No multi-body interactions
• No friction or material properties

2️⃣ Simulation Primitives (Requires Rapier Service)

Best for: Complex physics, multi-body dynamics, visualization data

Low-level building blocks for rigid-body simulations:

Tool	What It Does	When to Use
create_simulation	Initialize physics world	Start any simulation
add_rigid_body	Add objects (box, sphere, capsule, etc.)	Build scene
step_simulation	Advance time	Run physics
record_trajectory	Capture motion for R3F/Remotion	Generate animation data
destroy_simulation	Cleanup resources	End simulation

Characteristics:

• 🦀 Requires Rapier service
• 🔄 Stateful (track simulation ID)
• 🧩 Composable (combine for complex scenarios)
• 💪 Full rigid-body dynamics

Typical workflow:

# 1. Create world
sim = create_simulation(gravity_y=-9.81)

# 2. Add objects
add_rigid_body(sim.sim_id, "ground", type="static", shape="plane")
add_rigid_body(sim.sim_id, "ball", type="dynamic", shape="sphere",
               radius=0.5, position=[0, 5, 0])

# 3. Run simulation
step_simulation(sim.sim_id, steps=300)

# 4. Record for visualization
trajectory = record_trajectory(sim.sim_id, "ball", steps=300)

# 5. Cleanup
destroy_simulation(sim.sim_id)

---

🎓 Example Conversations

Projectile Motion

User: A golf ball is hit at 70 m/s at 15 degrees. How far does it go and what's the flight time?

LLM: [calls calculate_projectile_motion(70, 15)]

     Range ≈ 249.7 m
     Time of flight ≈ 3.694 s
     Maximum height ≈ 16.73 m

     Here are 50 trajectory points for visualization...

User: What angle gives maximum range for a cannonball fired at 100 m/s?

LLM: [calls calculate_projectile_motion multiple times with different angles]

     The optimal angle is 45°, which gives:
     - Maximum range: 1019.4 m
     - Flight time: 14.4 s
     - Peak height: 254.8 m

Collision Detection

User: Two asteroids: one at (0,0,0) moving at 1000 m/s in x direction,
      another at (50000, 100, 0) moving at -800 m/s in x. Will they collide?

LLM: [calls check_collision with both objects]

     Yes, they will collide!
     - Collision time: ~27.8 seconds
     - Impact location: (27,778, 100, 0)
     - Impact speed: 1,800 m/s
     - This would be a devastating impact given the closing speed.

User: Two cars 500m apart, one at 60 mph, other at 45 mph heading toward each other.
      When do they collide?

LLM: [calls check_collision]

     Collision in 10.7 seconds at position 250m from car 1's starting point.
     Combined approach speed is 105 mph (46.9 m/s).

Force & Energy Calculations

User: What force is needed to accelerate a 1500 kg car at 3 m/s²?

LLM: [calls calculate_force(1500, 3)]

     Force required: 4,500 N (Newtons)
     This is about 1,011 pounds of force.

User: What's the kinetic energy of a 2000 kg car at 30 m/s (about 67 mph)?

LLM: [calls calculate_kinetic_energy(2000, 30)]

     Kinetic energy: 900,000 J (Joules)
     That's 900 kJ or 0.25 kWh of energy.
     For perspective, this is why car crashes at highway speeds are so dangerous.

Momentum Analysis

User: Calculate momentum of a 70 kg runner at 10 m/s

LLM: [calls calculate_momentum(70, 10)]

     Momentum: 700 kg⋅m/s
     Direction: same as velocity vector

Complex Multi-Step Problems

User: I'm designing a water fountain. Water shoots up at 15 m/s.
      How high does it go and when does it come back down?

LLM: [calls calculate_projectile_motion(15, 90)]

     The water will:
     - Rise to maximum height: 11.47 m
     - Take 1.53 seconds to reach peak
     - Return to ground level after 3.06 seconds total
     - Have the same 15 m/s speed when it lands (just downward)

User: A basketball player shoots from 2m high at 7 m/s toward a hoop
      3.05m high and 4.6m away. What angles work?

LLM: [calls calculate_projectile_motion with various angles]

     Two possible angles work:
     1. Low arc: 38° (faster, flatter shot)
     2. High arc: 52° (slower, higher shot)

     Most players use 50-55° for better chance of going in.

Rigid Body Simulations

User: Create a simulation of a ball dropping from 10 meters and bouncing

LLM: [calls create_simulation(gravity_y=-9.81)]
     [calls add_rigid_body for ground plane (static)]
     [calls add_rigid_body for ball (dynamic, sphere, position=(0,10,0))]
     [calls record_trajectory(steps=200)]

     Simulation complete! The ball:
     - Hits ground at t=1.43s with velocity 14.0 m/s
     - Bounces to 7.5m (with restitution=0.8)
     - Makes 5 bounces before settling

     Here's the full trajectory data for R3F visualization...

User: Simulate 5 boxes stacked, then another box hits them from the side

LLM: [calls create_simulation]
     [calls add_rigid_body for ground]
     [calls add_rigid_body 5 times for stacked boxes]
     [calls add_rigid_body for projectile box with velocity]
     [calls step_simulation(300)]
     [calls record_trajectory for each box]

     The collision causes a realistic toppling effect!
     Boxes 1-2 fall left, boxes 3-5 scatter right.
     Peak chaos at t=0.8s. All settled by t=3.2s.

     Full trajectory data ready for 3D visualization...

---

💡 Try These Prompts

Copy and paste these into your LLM chat to see the physics tools in action:

Projectile Motion

• A golf ball is hit at 70 m/s at 15 degrees. How far does it go and what's the flight time?
• What angle gives maximum range for a cannonball fired at 100 m/s?
• If I throw a javelin at 28 m/s from 2 meters high, what angle gives maximum distance?
• A basketball player shoots from 2m high at 7 m/s toward a hoop 3.05m high and 4.6m away. What angles work?

Collision Detection

• Two cars 500m apart, one at 60 mph, other at 45 mph heading toward each other. When do they collide?
• Two asteroids: one at (0,0,0) moving at 1000 m/s in x direction, another at (50000, 100, 0) moving at -800 m/s in x. Will they collide?
• Spaceship A at (10000,0,0) moving at (-50,0,0) m/s, spaceship B at (-10000,100,0) moving at (45,0,0) m/s. Collision check?

Force, Energy & Momentum

• What force is needed to accelerate a 1500 kg car at 3 m/s²?
• What's the kinetic energy of a 2000 kg car traveling at 30 m/s?
• Calculate the momentum of a 70 kg runner sprinting at 10 m/s
• How much energy does a 0.145 kg baseball have when pitched at 45 m/s?

Real-World Applications

• I'm designing a water fountain. Water shoots up at 15 m/s. How high does it go?
• A cannon on a 50 meter cliff fires horizontally at 200 m/s. How far from the base does the projectile land?
• Two cars crash: Car A (1500kg) at 30 m/s, Car B (1200kg) at 25 m/s. What's the total kinetic energy at impact?

Simulations (Requires Rapier Service)

• Create a simulation of a ball dropping from 10 meters height and bouncing on the ground
• Simulate 5 boxes stacked on top of each other, then have another box hit them from the side
• Create a Newton's cradle with 5 spheres and record their motion
• Simulate a sphere rolling down a 30-degree ramp

---

📝 Example Scripts

The examples/ directory contains working demonstration scripts:

Ready to Run (No External Services Required)

These examples use the built-in analytic provider and work immediately:

• 00_quick_start.py - Quick demo of all 5 analytic tools
• 01_simple_projectile.py - Cannonball trajectories, basketball shots, angle comparisons
• 02_collision_detection.py - Car crashes, near misses, asteroid collisions
• 03_force_energy_momentum.py - F=ma, kinetic energy, momentum conservation
• 04_r3f_visualization.py - Generate React Three Fiber visualization data

# Run any example
python examples/00_quick_start.py
python examples/01_simple_projectile.py
# ... etc

Requires Rapier Service

This example demonstrates rigid-body simulations but needs the Rapier service running:

• 05_rapier_simulation.py - Bouncing balls, collisions, stacking boxes

# Start Rapier service first (see RAPIER_SERVICE.md)
docker run -p 9000:9000 chuk-rapier-service

# Then run example
python examples/05_rapier_simulation.py

Note: Examples 00-04 provide full functionality for calculations, collisions, and trajectory generation. Example 05 showcases advanced rigid-body physics when you have the Rapier service available.

---

⚙️ Configuration

Environment Variables

# Provider selection
PHYSICS_PROVIDER=analytic          # or "rapier"

# Rapier service (only if using Rapier provider)
# The default is automatically determined:
# - On Fly.io: uses https://rapier.chukai.io (public service)
# - Locally: uses http://localhost:9000
#
# Override with:
RAPIER_SERVICE_URL=https://rapier.chukai.io  # or http://localhost:9000

# Optional configuration
RAPIER_TIMEOUT=30.0
RAPIER_MAX_RETRIES=3
RAPIER_RETRY_DELAY=1.0

YAML Configuration

Create physics.yaml in your working directory or ~/.config/chuk-mcp-physics/:

default_provider: rapier

providers:
  # Override provider per tool type
  simulations: rapier
  projectile_motion: analytic

rapier:
  # Public service (recommended)
  service_url: https://rapier.chukai.io

  # Or local development
  # service_url: http://localhost:9000

  timeout: 30.0
  max_retries: 3
  retry_delay: 1.0

---

🛡️ Safety & Limits

Recommended Ranges

Understanding these limits helps prevent timeouts, instabilities, and confusion:

Parameter	Recommended	Maximum	Notes
Units	meters, kg, seconds	-	SI units throughout
dt	0.008 - 0.033	0.001 - 0.1	<0.008 = overkill, >0.033 = unstable
steps	100 - 5000	10,000	Depends on dt and complexity
bodies	1 - 100	1,000	Performance degrades >100
gravity	-20 to 0 m/s²	-100 to +100	Earth = -9.81
velocity	0 - 100 m/s	1,000 m/s	Very high speeds may cause tunneling
mass	0.1 - 10,000 kg	1e-6 - 1e6	Extreme ratios cause instability

Public Service Limits

The public Rapier service at https://rapier.chukai.io has these limits:

• Max steps per call: 5,000
• Max bodies per simulation: 100
• Max concurrent simulations: 10 per IP
• Request timeout: 30 seconds
• Max simulation lifetime: 1 hour (auto-cleanup)

For larger simulations, run your own Rapier service (see RAPIER_SERVICE.md).

Common Pitfalls

❌ Simulation explodes or bodies fly away

Symptoms:

• Bodies gain extreme velocities
• Objects disappear from view
• NaN values in positions

Causes:

• dt too large for the forces involved
• Very high mass ratios (1g object hitting 1000kg object)
• Extreme initial velocities

Solutions:

• Reduce dt to 0.008 or lower
• Use more similar masses (within 2-3 orders of magnitude)
• Limit initial velocities to <100 m/s

❌ Simulation runs very slowly

Symptoms:

• Request takes >10 seconds
• Timeout errors
• High CPU usage

Causes:

• Too many bodies (>100)
• Very small dt (<0.005)
• Complex mesh colliders
• Too many steps (>5000)

Solutions:

• Reduce body count or simplify shapes
• Increase dt (balance accuracy vs. speed)
• Break large step counts into multiple calls
• Use primitive shapes (sphere, box) instead of meshes

❌ Objects tunnel through each other

Symptoms:

• Fast-moving objects pass through walls
• Collisions not detected
• Objects appear inside each other

Causes:

• Very high velocities + large dt
• Thin colliders (<0.1m)
• Disabled continuous collision detection (CCD)

Solutions:

• Reduce dt for high-speed scenarios
• Thicken colliders (minimum 0.1m recommended)
• Reduce velocities
• Enable CCD if available (future feature)

Best Practices

1. Start simple: Test with 2-3 bodies before scaling up
2. Validate inputs: Check for NaN, Inf, extreme values before simulation
3. Monitor performance: Track step time, adjust dt/steps accordingly
4. Cleanup: Always destroy_simulation when done (prevent memory leaks)
5. Use analytic when possible: For simple scenarios, analytic is faster and exact

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🔧 Development

# Clone repository
git clone https://github.com/yourusername/chuk-mcp-physics
cd chuk-mcp-physics

# Install in development mode
make dev-install

# Run tests
make test

# Run tests with coverage
make test-cov

# Format code
make format

# Run all checks
make check

# Build package
make build

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🐳 Docker Deployment

# Build image
make docker-build

# Run container
make docker-run

# Access at http://localhost:8000

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☁️ Production Deployment

Live Public Services

Current Production Services:

• MCP Physics Server: https://physics.chukai.io/mcp

  • Public hosted MCP server endpoint
  • No local installation required
  • Pre-configured with Rapier service
  • Ready to use in Claude Desktop

• Rapier Physics Engine: https://rapier.chukai.io

  • Public API for physics simulations
  • No authentication required for basic usage
  • Rate limits may apply

Quick Test:

# Test the public Rapier service
curl https://rapier.chukai.io/health

# Use with chuk-mcp-physics locally
export RAPIER_SERVICE_URL=https://rapier.chukai.io
uvx chuk-mcp-physics

Deploy Your Own Rapier Service

If you need your own private Rapier service instance:

1. Deploy Rapier Service to Fly.io

cd rapier-service

# Login to Fly.io
fly auth login

# Create and deploy
fly apps create your-rapier-physics
fly deploy

# Add custom domain (optional)
fly certs add rapier.yourdomain.com -a your-rapier-physics

# Verify
curl https://your-rapier-physics.fly.dev/health

2. Configure chuk-mcp-physics to Use Your Service

# Option 1: Environment variable
export RAPIER_SERVICE_URL=https://rapier.yourdomain.com
uvx chuk-mcp-physics

# Option 2: YAML config (physics.yaml)
# rapier:
#   service_url: https://rapier.yourdomain.com

Why deploy your own?

• 🔒 Private instance for production workloads
• 📈 Custom scaling and resource allocation
• 🌍 Deploy closer to your users (different regions)
• 💾 Persistent simulations and custom configurations

See DEPLOYMENT.md for complete deployment guide, scaling strategies, and CI/CD setup.

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🦀 Rapier Service Setup

For full rigid-body simulations, you have several options:

Option 1: Use Public Service (Easiest)

# No setup required - just configure the URL
export RAPIER_SERVICE_URL=https://rapier.chukai.io
uvx chuk-mcp-physics

Option 2: Run Locally with Docker

# Using Docker
docker run -p 9000:9000 chuk-rapier-service

# Configure to use local service
export RAPIER_SERVICE_URL=http://localhost:9000
uvx chuk-mcp-physics

Option 3: Build from Source

# Build and run the Rust service
cd rapier-service
cargo run --release

# In another terminal
export RAPIER_SERVICE_URL=http://localhost:9000
uvx chuk-mcp-physics

See RAPIER_SERVICE.md for:

• Complete API specification
• Rust implementation guide
• Docker deployment details
• Testing examples

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📊 Comparison: Analytic vs Rapier

Feature	Analytic Provider	Rapier Provider
Projectile motion	✅ Exact (kinematic eqs)	✅ Simulated
Simple collisions	✅ Exact (sphere-sphere)	✅ Simulated
Force/energy/momentum	✅ Direct calculation	✅ Can derive from sim
Rigid-body dynamics	❌ Not supported	✅ Full 3D/2D physics
Complex shapes	❌ Spheres only	✅ Box, capsule, mesh, etc
Friction/restitution	❌ Not modeled	✅ Full material properties
Multi-body systems	❌ Not supported	✅ Unlimited bodies
Constraints/joints	❌ Not supported	✅ Hinges, sliders, etc
Performance	⚡ Instant	🐇 Fast (Rust)
Setup	📦 Built-in	🦀 Requires Rapier service

Recommendation:

• Use Analytic for simple calculations, education, quick answers
• Use Rapier for complex simulations, games, visualizations, multi-body dynamics

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🤝 Contributing

Contributions welcome! Please see CONTRIBUTING.md.

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📄 License

MIT License - see LICENSE for details.

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🙏 Acknowledgments

• Rapier - Fast 2D/3D physics engine in Rust
• chuk-mcp-server - MCP server framework
• Model Context Protocol - Protocol specification

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📚 See Also

• RAPIER_SERVICE.md - Rapier microservice specification
• Examples - Example usage patterns
• API Documentation - Detailed tool reference