Raibid Labs Documentation

implementation-plan

16 min read

AI-Powered TUI Bevy Reference Implementation Plan

Project Overview

Goal: Build a reference implementation demonstrating AI prompts → Bevy MCP (BRP) → TUI rendering

Foundation: Based on bevy-mcp-ref with bevy_ratatui_camera integration

Key Value Proposition: Enable AI assistants to control and visualize Bevy applications in terminal environments, providing headless operation capabilities and ASCII art rendering.

Phase 1: Foundation Setup

Objective: Establish project structure with core dependencies and basic TUI rendering capability

Tasks

1.1 Project Initialization and Dependencies

  • Complexity: Simple

  • Acceptance Criteria:

    • Project forked/cloned from bevy-mcp-ref
    • Cargo.toml updated with all required dependencies
    • Feature flags configured for flexible builds
    • Project compiles successfully with base dependencies

  • Files to Create/Modify:

    • Cargo.toml - Add bevy_ratatui_camera, ratatui, crossterm dependencies
    • .gitignore - Ensure build artifacts excluded

  • Dependencies Required:

    [dependencies]
bevy = { version = "0.16", features = ["dynamic_linking"] }
bevy_brp_extras = { version = "0.2", optional = true }
bevy_ratatui_camera = { version = "0.14", optional = true }
bevy_ratatui = { version = "0.9", optional = true }
ratatui = { version = "0.30", optional = true }
crossterm = { version = "0.28", optional = true }
 
[features]
default = []
brp = ["bevy/bevy_remote", "bevy_brp_extras"]
tui = ["bevy_ratatui_camera", "bevy_ratatui", "ratatui", "crossterm"]
full = ["brp", "tui"]

  • Testing Strategy: cargo check --all-features passes

1.2 Project Structure Organization

  • Complexity: Simple

  • Acceptance Criteria:

    • Directory structure follows Rust best practices
    • Clear separation of concerns between modules
    • Documentation directories created
    • Example directories organized

  • Files to Create/Modify:

    bevy-mcp-ratatui-ref/
├── src/
│   ├── lib.rs              # Library entry point
│   ├── main.rs             # Binary entry point
│   ├── tui/
│   │   ├── mod.rs          # TUI module
│   │   ├── plugin.rs       # RatatuiCameraPlugin wrapper
│   │   └── config.rs       # TUI configuration
│   ├── brp/
│   │   ├── mod.rs          # BRP module
│   │   └── tools.rs        # Custom MCP tools
│   └── systems/
│       ├── mod.rs          # Game systems
│       └── demo.rs         # Demo scene systems
├── examples/
│   ├── tui_basic.rs        # Basic TUI example
│   ├── tui_brp.rs          # TUI + BRP example
│   └── windowed_tui.rs     # Windowed + TUI dual mode
├── docs/
│   ├── implementation-plan.md
│   ├── TUI_GUIDE.md
│   ├── AI_PROMPTS.md
│   └── ARCHITECTURE.md
└── tests/
    ├── integration_tests.rs
    └── tui_tests.rs

  • Testing Strategy: Directory structure verified, all module declarations compile

1.3 Basic TUI Rendering Plugin

  • Complexity: Medium

  • Acceptance Criteria:

    • RatatuiCameraPlugin wrapper created
    • Simple TUI rendering pipeline functional
    • Camera component properly configured
    • Terminal output visible and stable

  • Files to Create/Modify:

    • src/tui/mod.rs - Public module interface
    • src/tui/plugin.rs - Plugin implementation
    • src/tui/config.rs - TUI configuration structures

  • Implementation Details:

    // src/tui/plugin.rs
use bevy::prelude::*;
use bevy_ratatui_camera::{RatatuiCameraPlugin, RatatuiCamera};
 
pub struct BevyMcpTuiPlugin {
    pub enable_terminal_output: bool,
    pub render_scale: f32,
}
 
impl Plugin for BevyMcpTuiPlugin {
    fn build(&self, app: &mut App) {
        if self.enable_terminal_output {
            app.add_plugins(RatatuiCameraPlugin)
                .add_systems(Startup, setup_tui_camera);
        }
    }
}
 
fn setup_tui_camera(mut commands: Commands) {
    commands.spawn((
        Camera3d::default(),
        Transform::from_xyz(0.0, 5.0, 10.0)
            .looking_at(Vec3::ZERO, Vec3::Y),
        RatatuiCamera,
        Name::new("TUI Camera"),
    ));
}

  • Testing Strategy:

    • Manual: Run with TUI feature, verify terminal output
    • Automated: Unit tests for plugin registration

Phase 2: Core Integration

Objective: Integrate bevy_ratatui_camera with BRP, enabling dual rendering modes and camera synchronization

Dependencies

  • Requires: Phase 1 complete

Tasks

2.1 bevy_ratatui_camera Integration

  • Complexity: Medium

  • Acceptance Criteria:

    • RatatuiCamera component properly integrated
    • Rendering strategies configurable (ASCII, Unicode, Braille)
    • Depth detection enabled and functional
    • Performance optimized for real-time rendering

  • Files to Create/Modify:

    • src/tui/rendering.rs - Rendering strategy management
    • src/tui/widget.rs - Custom ratatui widgets
    • examples/tui_basic.rs - Basic TUI example

  • Implementation Details:

    // src/tui/rendering.rs
use bevy_ratatui_camera::{RatatuiCamera, RenderingStrategy};
 
#[derive(Component, Reflect, Clone, Copy)]
pub enum TuiRenderMode {
    Ascii,
    Unicode,
    Braille,
    Auto, // Select based on terminal capabilities
}
 
pub fn apply_render_mode(
    mut cameras: Query<&mut RatatuiCamera>,
    config: Res<TuiConfig>,
) {
    for mut camera in cameras.iter_mut() {
        camera.rendering_strategy = match config.render_mode {
            TuiRenderMode::Ascii => RenderingStrategy::Ascii,
            TuiRenderMode::Unicode => RenderingStrategy::Unicode,
            TuiRenderMode::Braille => RenderingStrategy::Braille,
            TuiRenderMode::Auto => detect_terminal_capabilities(),
        };
    }
}

  • Testing Strategy:

    • Visual verification of different rendering modes
    • Performance benchmarks for rendering strategies
    • Terminal compatibility tests

2.2 BRP + TUI Dual Rendering Mode

  • Complexity: Complex

  • Acceptance Criteria:

    • Both windowed and TUI rendering work simultaneously
    • Headless TUI-only mode functional
    • Mode switching via configuration
    • No visual artifacts or performance degradation

  • Files to Create/Modify:

    • src/lib.rs - Dual mode configuration
    • src/tui/dual_mode.rs - Dual rendering coordination
    • examples/windowed_tui.rs - Windowed + TUI example

  • Implementation Details:

    // src/tui/dual_mode.rs
pub enum RenderingMode {
    WindowedOnly,
    TuiOnly,
    Dual { sync_cameras: bool },
}
 
pub struct DualModePlugin {
    pub mode: RenderingMode,
}
 
impl Plugin for DualModePlugin {
    fn build(&self, app: &mut App) {
        match self.mode {
            RenderingMode::WindowedOnly => {
                app.add_plugins(DefaultPlugins);
            }
            RenderingMode::TuiOnly => {
                app.add_plugins(MinimalPlugins)
                    .add_plugins(RatatuiCameraPlugin);
            }
            RenderingMode::Dual { sync_cameras } => {
                app.add_plugins(DefaultPlugins)
                    .add_plugins(RatatuiCameraPlugin);
                if sync_cameras {
                    app.add_systems(Update, sync_camera_transforms);
                }
            }
        }
    }
}

  • Testing Strategy:

    • Test all three rendering modes independently
    • Verify camera synchronization in dual mode
    • Performance profiling for each mode

2.3 Camera Synchronization System

  • Complexity: Medium

  • Acceptance Criteria:

    • TUI camera mirrors windowed camera position/rotation
    • Synchronization can be toggled at runtime
    • Independent camera control option available
    • Smooth transitions without jitter

  • Files to Create/Modify:

    • src/systems/camera_sync.rs - Camera synchronization logic
    • src/tui/config.rs - Add sync configuration

  • Implementation Details:

    // src/systems/camera_sync.rs
#[derive(Component)]
pub struct MainCamera;
 
#[derive(Component)]
pub struct SyncedTuiCamera {
    pub sync_enabled: bool,
    pub offset: Transform,
}
 
pub fn sync_camera_transforms(
    main_camera: Query<&Transform, (With<MainCamera>, Without<RatatuiCamera>)>,
    mut tui_cameras: Query<(&mut Transform, &SyncedTuiCamera), With<RatatuiCamera>>,
) {
    if let Ok(main_transform) = main_camera.get_single() {
        for (mut tui_transform, synced) in tui_cameras.iter_mut() {
            if synced.sync_enabled {
                *tui_transform = *main_transform * synced.offset;
            }
        }
    }
}

  • Testing Strategy:

    • Unit tests for transform calculations
    • Visual verification of synchronized movement
    • Test edge cases (camera deletion, multiple cameras)

Phase 3: MCP Enhancement

Objective: Extend BRP with AI-friendly tools for TUI control and entity manipulation

Dependencies

  • Requires: Phase 2 complete

Tasks

3.1 AI-Friendly Entity Naming Convention

  • Complexity: Simple

  • Acceptance Criteria:

    • All entities have descriptive Name components
    • Naming convention documented
    • Helper functions for name generation
    • Query by name functionality added

  • Files to Create/Modify:

    • src/brp/naming.rs - Naming utilities
    • docs/NAMING_CONVENTION.md - Naming guidelines

  • Implementation Details:

    // src/brp/naming.rs
pub struct NameBuilder {
    category: String,
    index: Option<usize>,
    descriptor: Option<String>,
}
 
impl NameBuilder {
    pub fn new(category: impl Into<String>) -> Self {
        Self {
            category: category.into(),
            index: None,
            descriptor: None,
        }
    }
 
    pub fn with_index(mut self, index: usize) -> Self {
        self.index = Some(index);
        self
    }
 
    pub fn with_descriptor(mut self, desc: impl Into<String>) -> Self {
        self.descriptor = Some(desc.into());
        self
    }
 
    pub fn build(self) -> Name {
        let mut name = self.category;
        if let Some(idx) = self.index {
            name.push_str(&format!(" {}", idx));
        }
        if let Some(desc) = self.descriptor {
            name.push_str(&format!(" ({})", desc));
        }
        Name::new(name)
    }
}
 
// Examples:
// "Sphere 1 (Red)"
// "Tree 3 (Oak)"
// "Camera (TUI)"

  • Testing Strategy: Unit tests for name generation and queries

3.2 Custom MCP Tools for TUI Control

  • Complexity: Complex

  • Acceptance Criteria:

    • Custom tools registered with bevy_brp_extras
    • TUI rendering mode switchable via MCP
    • Camera control tools functional
    • Tools documented with examples

  • Files to Create/Modify:

    • src/brp/tools.rs - Custom MCP tool implementations
    • src/brp/mod.rs - Tool registration
    • docs/MCP_TOOLS.md - Tool documentation

  • Custom Tools to Implement:

    1. tui/set_render_mode - Change rendering strategy
    2. tui/toggle_output - Enable/disable TUI output
    3. tui/get_camera_config - Query TUI camera settings
    4. tui/set_camera_sync - Control camera synchronization
    5. tui/capture_frame - Capture current TUI frame as string
    6. entity/find_by_name - Query entities by name pattern
    7. entity/list_named - List all named entities

  • Implementation Details:

    // Example custom tool
pub fn register_tui_tools(app: &mut App) {
    app.add_brp_tool("tui/set_render_mode", set_render_mode_tool)
       .add_brp_tool("tui/capture_frame", capture_frame_tool)
       .add_brp_tool("entity/find_by_name", find_by_name_tool);
}
 
fn set_render_mode_tool(
    In(params): In<SetRenderModeParams>,
    mut config: ResMut<TuiConfig>,
) -> Result<(), BrpError> {
    config.render_mode = params.mode;
    Ok(())
}

  • Testing Strategy:

    • Integration tests for each tool
    • Test via actual MCP calls from Claude
    • Error handling verification

3.3 Rendering Strategy Selection via MCP

  • Complexity: Medium

  • Acceptance Criteria:

    • AI can switch between ASCII/Unicode/Braille modes
    • Terminal capability detection works
    • Mode changes apply without restart
    • Visual feedback of mode changes

  • Files to Create/Modify:

    • src/tui/strategy.rs - Strategy selection logic
    • src/brp/tools.rs - Add strategy switching tool

  • Implementation Details:

    // MCP tool for AI to switch rendering modes
#[derive(Serialize, Deserialize)]
pub struct SetRenderStrategyParams {
    pub strategy: String, // "ascii" | "unicode" | "braille" | "auto"
    pub apply_immediately: bool,
}
 
pub fn set_render_strategy(
    In(params): In<SetRenderStrategyParams>,
    mut cameras: Query<&mut RatatuiCamera>,
) -> Result<String, BrpError> {
    let strategy = match params.strategy.as_str() {
        "ascii" => RenderingStrategy::Ascii,
        "unicode" => RenderingStrategy::Unicode,
        "braille" => RenderingStrategy::Braille,
        "auto" => detect_best_strategy(),
        _ => return Err(BrpError::InvalidParams),
    };
 
    for mut camera in cameras.iter_mut() {
        camera.rendering_strategy = strategy;
    }
 
    Ok(format!("Rendering strategy set to: {}", params.strategy))
}

  • Testing Strategy:

    • Test each rendering strategy switch
    • Verify terminal detection accuracy
    • Performance testing for mode switches

Phase 4: Examples & Documentation

Objective: Create comprehensive examples and guides demonstrating AI-driven TUI control

Dependencies

  • Requires: Phase 3 complete

Tasks

4.1 Interactive TUI Demo Scene

  • Complexity: Medium

  • Acceptance Criteria:

    • Rich 3D scene visible in terminal
    • Interactive elements controllable via AI
    • Performance optimized for smooth rendering
    • Demonstrates all key features

  • Files to Create/Modify:

    • examples/tui_interactive_demo.rs - Main demo
    • src/systems/demo.rs - Demo scene systems
    • assets/scenes/tui_demo.ron - Scene configuration (optional)

  • Demo Scene Features:

    • 5-10 named entities (spheres, cubes, cylinders)
    • Animated elements (rotating, bouncing, orbiting)
    • Multiple cameras (main, TUI, debug)
    • Lighting setup optimized for TUI visibility
    • Interactive controls (keyboard + MCP)
    • Status display showing FPS, entity count, camera info

  • Implementation Details:

    // examples/tui_interactive_demo.rs
fn main() {
    App::new()
        .add_plugins((
            MinimalPlugins,
            RatatuiCameraPlugin,
            BrpExtrasPlugin,
        ))
        .insert_resource(TuiConfig {
            render_mode: TuiRenderMode::Auto,
            target_fps: 30,
            ..default()
        })
        .add_systems(Startup, (
            setup_demo_scene,
            setup_tui_cameras,
            setup_brp_server,
        ))
        .add_systems(Update, (
            rotate_entities,
            bounce_entities,
            orbit_entities,
            update_status_display,
        ))
        .run();
}

  • Testing Strategy:

    • Manual testing in various terminals
    • Performance profiling (target 30 FPS in terminal)
    • Cross-platform testing (macOS, Linux, Windows)

4.2 AI Prompt Examples Collection

  • Complexity: Simple

  • Acceptance Criteria:

    • 20+ documented prompt examples
    • Prompts categorized by use case
    • Expected outputs documented
    • Troubleshooting guide included

  • Files to Create/Modify:

    • docs/AI_PROMPTS.md - Comprehensive prompt guide
    • docs/PROMPT_PATTERNS.md - Common patterns and best practices

  • Prompt Categories:

    1. Scene Inspection

      • “Show me all entities in the TUI view”
      • “What’s the current rendering mode?”
      • “List all cameras and their positions”

    2. Entity Manipulation

      • “Make the red sphere twice as large”
      • “Move the blue cube to position (5, 0, 0)”
      • “Change the tree’s color to dark green”

    3. Camera Control

      • “Switch the TUI camera to ASCII mode”
      • “Move the TUI camera to look down from above”
      • “Enable camera synchronization between window and TUI”

    4. Scene Creation

      • “Spawn a golden sphere at the center”
      • “Create a ring of 8 colored cubes around the origin”
      • “Add a spotlight pointing at the main cube”

    5. Performance & Debugging

      • “Show current FPS and rendering stats”
      • “What entities are visible to the TUI camera?”
      • “Capture the current TUI frame as text”

  • Testing Strategy: Verify each prompt works with actual AI assistant

4.3 Comprehensive User Guides

  • Complexity: Simple

  • Acceptance Criteria:

    • Installation guide complete
    • Quick start tutorial functional
    • API reference accurate
    • Troubleshooting section comprehensive

  • Files to Create/Modify:

    • docs/TUI_GUIDE.md - Complete TUI usage guide
    • docs/ARCHITECTURE.md - System architecture documentation
    • docs/TROUBLESHOOTING.md - Common issues and solutions
    • README.md - Update with TUI features

  • Documentation Structure:

    TUI_GUIDE.md:

    • Introduction to TUI rendering in Bevy
    • Installation and setup
    • Configuration options
    • Rendering modes explained
    • Camera setup and controls
    • Performance tuning
    • Terminal compatibility

    ARCHITECTURE.md:

    • System overview diagram
    • Component relationships
    • Data flow: AI → MCP → BRP → Bevy → TUI
    • Plugin architecture
    • Custom tool implementation guide

    TROUBLESHOOTING.md:

    • Common issues and solutions
    • Performance optimization tips
    • Terminal compatibility fixes
    • BRP connection issues
    • Rendering artifacts solutions

  • Testing Strategy: Technical review, user testing with fresh installation

Phase 5: Testing & Polish

Objective: Ensure production quality through comprehensive testing and refinement

Dependencies

  • Requires: Phase 4 complete

Tasks

5.1 Integration Test Suite

  • Complexity: Complex

  • Acceptance Criteria:

    • 90%+ code coverage for critical paths
    • All rendering modes tested
    • BRP integration tests passing
    • Cross-platform tests successful

  • Files to Create/Modify:

    • tests/integration_tests.rs - Main integration tests
    • tests/tui_rendering_tests.rs - TUI-specific tests
    • tests/brp_tools_tests.rs - MCP tool tests
    • tests/camera_sync_tests.rs - Camera synchronization tests

  • Test Categories:

    1. TUI Rendering Tests

      #[test]
fn test_ascii_rendering_mode() {
    let app = create_test_app(RenderMode::Ascii);
    // Verify ASCII characters in output
}
 
#[test]
fn test_unicode_rendering_mode() {
    let app = create_test_app(RenderMode::Unicode);
    // Verify Unicode characters in output
}
 
#[test]
fn test_braille_rendering_mode() {
    let app = create_test_app(RenderMode::Braille);
    // Verify Braille characters in output
}

    2. BRP Integration Tests

      #[test]
fn test_set_render_mode_via_brp() {
    // Test MCP tool calls
}
 
#[test]
fn test_find_entity_by_name() {
    // Test entity querying
}

    3. Camera Synchronization Tests

      #[test]
fn test_camera_sync_enabled() {
    // Verify cameras stay synchronized
}
 
#[test]
fn test_camera_sync_disabled() {
    // Verify independent camera movement
}

  • Testing Strategy:

    • Unit tests for individual components
    • Integration tests for system interactions
    • End-to-end tests with actual BRP calls
    • Performance regression tests

5.2 Performance Optimization

  • Complexity: Medium

  • Acceptance Criteria:

    • TUI rendering maintains 30 FPS minimum
    • Memory usage stable over time
    • BRP latency < 50ms for simple operations
    • No memory leaks detected

  • Files to Create/Modify:

    • benches/rendering_bench.rs - Performance benchmarks
    • src/tui/optimization.rs - Performance optimizations
    • docs/PERFORMANCE.md - Performance guide

  • Optimization Areas:

    1. Rendering Performance

      • Frame caching for static scenes
      • Culling for off-screen entities
      • Adaptive rendering quality based on FPS
      • Batch character updates

    2. Memory Management

      • Reuse buffers for character arrays
      • Limit history/frame buffer size
      • Clean up unused resources

    3. BRP Communication

      • Response caching for repeated queries
      • Batch operations where possible
      • Async processing for heavy operations

  • Benchmarking:

    // benches/rendering_bench.rs
fn bench_ascii_rendering(c: &mut Criterion) {
    c.bench_function("ascii_render_100_entities", |b| {
        b.iter(|| {
            // Benchmark rendering
        });
    });
}

  • Testing Strategy:

    • Run benchmarks on different hardware
    • Profile with cargo flamegraph
    • Memory profiling with valgrind/heaptrack
    • Stress tests with 1000+ entities

5.3 Documentation Refinement

  • Complexity: Simple

  • Acceptance Criteria:

    • All code examples tested and working
    • API documentation complete
    • Screenshots/GIFs added where helpful
    • External review completed

  • Files to Create/Modify:

    • All docs/* files - Final polish
    • CONTRIBUTING.md - Contribution guidelines
    • CHANGELOG.md - Version history
    • LICENSE - License file

  • Documentation Improvements:

    • Add terminal recording GIFs to README
    • Create video walkthrough for YouTube
    • Add architecture diagrams (mermaid)
    • Create API reference docs
    • Add code comments and rustdoc

  • Documentation Checklist:

    • All code examples compile and run
    • All links working
    • Consistent terminology throughout
    • Adequate troubleshooting coverage
    • Installation tested on fresh systems
    • AI prompt examples verified

  • Testing Strategy:

    • Documentation review by external developer
    • Fresh installation test on multiple platforms
    • Verify all examples in docs/

Cross-Cutting Concerns

Error Handling Strategy

  • All BRP tools return Result types with descriptive errors
  • TUI rendering failures degrade gracefully (fallback to simpler mode)
  • Logging at appropriate levels (debug, info, warn, error)
  • User-friendly error messages with recovery suggestions

Logging and Debugging

  • Structured logging with tracing crate
  • Debug overlays for TUI rendering
  • BRP request/response logging option
  • Performance metrics logging

Configuration Management

  • TOML-based configuration files
  • Environment variable overrides
  • Runtime configuration via MCP tools
  • Sensible defaults for all settings

Platform Compatibility

  • Primary: macOS, Linux
  • Secondary: Windows (with known terminal limitations)
  • Terminal capability detection
  • Graceful degradation for unsupported features

Success Metrics

Technical Metrics

  • All feature flags compile independently
  • Test coverage > 85% for core functionality
  • TUI rendering maintains 30+ FPS with 100 entities
  • BRP latency < 50ms for 95% of operations
  • Zero memory leaks over 1-hour stress test
  • Works on macOS, Linux, and Windows terminals

User Experience Metrics

  • Fresh installation to “Hello World” < 5 minutes
  • AI can successfully control app with basic prompts
  • Documentation clear enough for Bevy beginners
  • At least 3 impressive demo scenes included
  • Terminal recording demonstrates wow factor

Community Metrics

  • GitHub repository with comprehensive README
  • At least 5 documented use cases
  • Blog post or tutorial article published
  • Example integrations with popular Bevy plugins
  • Active issue template and contributing guide

Risk Assessment and Mitigation

Technical Risks

Risk 1: Terminal Compatibility Issues

  • Impact: High
  • Probability: Medium
  • Mitigation:
    • Implement capability detection
    • Provide fallback rendering modes
    • Document known terminal incompatibilities
    • Test on wide range of terminals (iTerm2, Alacritty, Windows Terminal, etc.)

Risk 2: Performance Degradation in TUI Mode

  • Impact: Medium
  • Probability: Medium
  • Mitigation:
    • Early performance benchmarking
    • Adaptive rendering quality
    • Frame rate limiting
    • Optimization passes in Phase 5

Risk 3: BRP API Changes in Bevy Updates

  • Impact: High
  • Probability: Low
  • Mitigation:
    • Pin to specific Bevy version initially
    • Monitor Bevy release notes
    • Maintain compatibility layer
    • Quick response to breaking changes

Risk 4: Complex Camera Synchronization Bugs

  • Impact: Medium
  • Probability: Medium
  • Mitigation:
    • Comprehensive test coverage for sync logic
    • Optional synchronization (fail-safe: independent cameras)
    • Extensive manual testing
    • Clear documentation of sync behavior

Project Risks

Risk 5: Scope Creep

  • Impact: Medium
  • Probability: High
  • Mitigation:
    • Strict adherence to phase boundaries
    • MVP features first, enhancements later
    • Clear acceptance criteria for each task
    • Regular progress reviews

Risk 6: Documentation Falling Behind Implementation

  • Impact: Medium
  • Probability: High
  • Mitigation:
    • Documentation as acceptance criteria for each task
    • Phase 4 dedicated to documentation
    • Examples included with each feature
    • Regular documentation reviews

Timeline Estimates

Phase 1: Foundation Setup - 2-3 days

  • 1.1: Project Init - 4 hours
  • 1.2: Structure - 2 hours
  • 1.3: Basic TUI Plugin - 8-10 hours

Phase 2: Core Integration - 4-5 days

  • 2.1: bevy_ratatui_camera - 10-12 hours
  • 2.2: Dual Rendering - 12-16 hours
  • 2.3: Camera Sync - 6-8 hours

Phase 3: MCP Enhancement - 3-4 days

  • 3.1: Entity Naming - 4 hours
  • 3.2: Custom Tools - 12-16 hours
  • 3.3: Strategy Selection - 4-6 hours

Phase 4: Examples & Documentation - 3-4 days

  • 4.1: Interactive Demo - 8-10 hours
  • 4.2: AI Prompts - 4-6 hours
  • 4.3: User Guides - 8-12 hours

Phase 5: Testing & Polish - 4-5 days

  • 5.1: Integration Tests - 12-16 hours
  • 5.2: Performance - 8-10 hours
  • 5.3: Documentation Polish - 4-6 hours

Total Estimated Time: 16-21 days

Phase Dependencies Graph

Phase 1 (Foundation)
    └─→ Phase 2 (Core Integration)
            └─→ Phase 3 (MCP Enhancement)
                    └─→ Phase 4 (Examples & Docs)
                            └─→ Phase 5 (Testing & Polish)

Each phase must be completed before the next begins. Within phases, tasks can be parallelized where dependencies allow.

Deliverables Summary

Code Deliverables

  • Rust library crate with TUI + BRP support
  • 3+ working examples demonstrating features
  • Comprehensive test suite (unit + integration)
  • Performance benchmarks
  • CI/CD configuration (GitHub Actions)

Documentation Deliverables

  • README.md with quick start guide
  • Complete API documentation (rustdoc)
  • TUI integration guide
  • AI prompt examples (20+)
  • Architecture documentation
  • Troubleshooting guide
  • Contributing guidelines

Demo Deliverables

  • Interactive TUI demo application
  • Terminal recording (asciinema/VHS)
  • Video walkthrough (5-10 minutes)
  • Blog post or tutorial article
  • Example AI conversation transcripts

Next Steps

  1. Review this plan with stakeholders/team
  2. Set up development environment with all dependencies
  3. Create project repository with initial structure
  4. Begin Phase 1.1 - Project initialization
  5. Schedule regular check-ins after each phase completion

Appendix: Key Technologies Reference

Bevy 0.16+

  • ECS Architecture: Entity-Component-System game engine
  • BRP: Bevy Remote Protocol for external control
  • Reflection System: Runtime type information

bevy_ratatui_camera

  • Version: 0.14+
  • Features: Depth detection, multiple rendering strategies
  • Rendering Modes: ASCII, Unicode, Braille

bevy_brp_extras

  • Version: 0.2+
  • Extended Tools: Component mutation, resource mutation
  • Discovery: Format discovery for easier AI integration

ratatui + crossterm

  • ratatui: Terminal UI framework
  • crossterm: Cross-platform terminal manipulation
  • Features: Rich text, layout, events, styling

MCP (Model Context Protocol)

  • Purpose: Standardized AI tool calling protocol
  • Server: bevy_brp_mcp for Bevy integration
  • Tools: Custom tools for game state manipulation

Document Version: 1.0 Last Updated: 2025-11-10 Status: Planning - Ready for Implementation

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