# AbletonMCP-AI - Consolidado de Cambios v0.1.1 + v0.1.2

**Fecha**: 2026-03-30  
**Agentes**: Kimi K2 (5 agentes desplegados por sprint)  
**Total de sprints**: 2 (v0.1.1 y v0.1.2)  
**Estado**: Código implementado ~85%, Validado parcialmente (~40% runtime verified)

---

## 📋 Resumen Ejecutivo

Este documento consolida todo el trabajo realizado en los sprints v0.1.1 y v0.1.2 del proyecto AbletonMCP-AI. Incluye:

- Todas las tareas completadas
- Archivos modificados con líneas específicas
- Código de cambios importantes
- Estado de validación
- Issues conocidos
- Próximos pasos recomendados

**Hallazgo clave**: El 80% del código estaba implementado pero sin validación runtime. El sprint v0.1.2 se enfocó en verificar la realidad vs. la documentación histórica.

---

## 🎯 Tareas Completadas

### Sprint v0.1.1 (5 tareas)

| # | Tarea | Estado | Archivos |
|---|-------|--------|----------|
| 1 | Arreglar `clear_all_tracks` | ✅ Implementado + ✅ Validado | `abletonmcp_init.py:2664-2698` |
| 2 | Backoff/retry/cache Z.ai | ✅ Implementado | `zai_judges.py` |
| 3 | Same-pack estricto atmos/vocal | ✅ Implementado | `sample_selector.py` |
| 4 | Groove extraction dembow | ✅ Implementado | `groove_extractor.py`, `audio_analyzer.py` |
| 5 | Smoke test async | ✅ Implementado | `temp\smoke_test_async.py` |

### Sprint v0.1.2 (5 tareas)

| # | Tarea | Estado | Archivos |
|---|-------|--------|----------|
| 1 | Validar clear_all_tracks runtime | ✅ Validado | `abletonmcp_init.py:529` (timeout fix) |
| 2 | End-to-end async real | ⚠️ Issue encontrado | `server.py` (blocking) |
| 3 | Expandir corpus groove | ✅ Expandido | `groove_extractor.py` (16 templates) |
| 4 | Selector por sección | ✅ Implementado | `sample_selector.py`, `pack_brain.py` |
| 5 | Documentación honesta | ✅ Actualizada | 3 archivos MD |

---

## 🔧 Cambios Detallados

### 1. clear_all_tracks - FIXED ✅

**Problema original**: Error blando "Couldn't delete track" al limpiar + timeout en sesiones grandes

**Solución aplicada**:

```python
# abletonmcp_init.py:529
# CAMBIO: Extender timeout para clear_all_tracks
if command_type in ("generate_track", "clear_all_tracks"):
    timeout_seconds = 180.0  # Era solo 10s
else:
    timeout_seconds = 10.0
```

```python
# abletonmcp_init.py:2664-2698
# _clear_all_tracks method - Lógica completa

def _clear_all_tracks(self, params):
    """Clear all tracks and leave exactly one empty track."""
    tracks_deleted = 0
    
    # Delete tracks from the end to avoid index shifting
    while len(self._song.tracks) > 1:
        track_idx = len(self._song.tracks) - 1
        self._song.delete_track(track_idx)
        tracks_deleted += 1
    
    # Clear the remaining track (can't delete last one)
    if len(self._song.tracks) == 1:
        track = self._song.tracks[0]
        
        # Clear all clip slots
        if hasattr(track, 'clip_slots'):
            for slot in track.clip_slots:
                if slot.has_clip:
                    slot.delete_clip()
        
        # Remove all devices
        if hasattr(track, 'devices'):
            while len(track.devices) > 0:
                track.delete_device(0)
        
        # Reset name and color
        track.name = "1-MIDI"
        if hasattr(track, 'color'):
            track.color = 0
    
    return {
        "status": "success",
        "tracks_deleted": tracks_deleted,
        "message": f"Cleared {tracks_deleted} tracks, left 1 empty track"
    }
```

**Validación**:
- ✅ 3 limpiezas consecutivas sin crash
- ✅ Sesiones de 16+ tracks limpiadas correctamente
- ✅ No más timeout en sesiones grandes
- ✅ `get_session_info` devuelve consistentemente 1 track

---

### 2. Z.ai Backoff/Retry/Cache - IMPLEMENTADO ✅

**Archivo**: `AbletonMCP_AI/AbletonMCP_AI/MCP_Server/zai_judges.py`

**Configuración**:
```python
# zai_judges.py:29-34
CACHE_TTL_SECONDS = 300  # 5 minutos
MAX_RETRIES = 3
BACKOFF_DELAYS = [1.0, 2.0, 4.0]  # Exponencial
```

**Cache con SHA256**:
```python
# zai_judges.py:37-53
def _generate_cache_key(self, system_prompt: str, payload: Dict) -> str:
    """Generate cache key from prompt and payload."""
    cache_data = {
        "prompt_prefix": system_prompt[:200],
        "genre": payload.get("genre", ""),
        "style": payload.get("style", ""),
        "bpm": payload.get("bpm", 0),
        "key": payload.get("key", ""),
        "judge_role": payload.get("judge_role", ""),
        "candidates": [c.get("id", "") for c in payload.get("candidates", [])[:4]]
    }
    json_str = json.dumps(cache_data, sort_keys=True)
    return hashlib.sha256(json_str.encode()).hexdigest()
```

**Retry loop con backoff**:
```python
# zai_judges.py:155-205
def _call(self, system_prompt: str, payload: Dict) -> Dict:
    """Call Z.ai API with retry and cache."""
    cache_key = self._generate_cache_key(system_prompt, payload)
    
    # Check cache first
    cached_result = self._get_cached_result(cache_key)
    if cached_result is not None:
        logger.debug(f"Cache hit for key: {cache_key[:8]}...")
        return cached_result
    
    # Try API with retries
    for attempt in range(1, MAX_RETRIES + 1):
        try:
            response = self._make_api_call(system_prompt, payload)
            self._set_cached_result(cache_key, response)
            return response
            
        except HTTPError as e:
            if e.code == 429:
                if attempt < MAX_RETRIES:
                    delay = BACKOFF_DELAYS[attempt - 1]
                    logger.warning(f"Judge API 429 on attempt {attempt}/{MAX_RETRIES}, retrying in {delay}s...")
                    time.sleep(delay)
                    continue
            raise
        except (URLError, TimeoutError) as e:
            if attempt < MAX_RETRIES:
                delay = BACKOFF_DELAYS[attempt - 1]
                logger.warning(f"Judge API error on attempt {attempt}: {e}, retrying...")
                time.sleep(delay)
                continue
            raise
    
    return {}  # Fallback empty
```

**Fallback heurístico**:
```python
# zai_judges.py:225-242
def judge_palette_candidates(self, candidates: List[Dict], context: Dict) -> Dict:
    """Judge palette candidates with API or heuristic fallback."""
    try:
        result = self._call(system_prompt, payload)
        if not result:
            # API failed - use heuristic fallback
            logger.warning("Z.ai judges failed, using heuristic fallback")
            return {
                "mode": "heuristic_fallback",
                "selected": candidates[0] if candidates else None,
                "directives": {
                    "rhythm_density": "moderate",
                    "bass_motion": "rolling",
                    "arrangement_emphasis": "balanced",
                    "vocal_strategy": "sparse"
                }
            }
        return result
    except Exception as e:
        logger.error(f"Judge panel failed: {e}")
        return {"mode": "error", "selected": candidates[0] if candidates else None}
```

**Estado**: Implementado, necesita validación contra API real con 429

---

### 3. Same-Pack Selection - IMPLEMENTADO ✅

**Archivo**: `AbletonMCP_AI/AbletonMCP_AI/MCP_Server/sample_selector.py`

**Roles con same-pack estricto**:
```python
# sample_selector.py:1222-1243
SAME_PACK_STRICT_ROLES = [
    'atmos_fx',      # Atmósferas
    'vocal_shot',    # Vocales one-shot
    'fill_fx',       # FX de transición (NUEVO v0.1.2)
    'snare_roll'     # Redobles (NUEVO v0.1.2)
]
```

**Bonus/penalty system**:
```python
# sample_selector.py:1578-1632
def _calculate_same_pack_strict_bonus(
    self, 
    sample_path: str, 
    main_pack_folders: List[str]
) -> Tuple[float, str, str]:
    """
    Calculate bonus for selecting from same pack.
    
    Returns:
        (bonus_multiplier, selection_type, reason)
    """
    if not main_pack_folders:
        return 1.0, "neutral", "No main pack context"
    
    sample_folder = os.path.dirname(sample_path)
    sample_parts = Path(sample_folder).parts
    
    for main_folder in main_pack_folders:
        main_parts = Path(main_folder).parts
        
        # Check relationships
        if sample_folder == main_folder:
            return 2.0, "same_pack", "Exact folder match"
        
        if sample_folder.startswith(main_folder + os.sep):
            return 1.8, "same_pack", "Subfolder of main pack"
        
        # Check if same parent (sibling folders)
        if len(sample_parts) > 1 and len(main_parts) > 1:
            if sample_parts[-2] == main_parts[-2]:
                return 1.5, "same_parent", "Sibling folder (same parent)"
        
        # Check if same grandparent (cousin folders)
        if len(sample_parts) > 2 and len(main_parts) > 2:
            if sample_parts[-3] == main_parts[-3]:
                return 1.3, "same_grandparent", "Cousin folder (shared grandparent)"
    
    # Different pack - penalty
    return 0.4, "fallback", "Cross-pack selection"
```

**Section-aware selection** (NUEVO v0.1.2):
```python
# sample_selector.py:750-806
SECTION_ROLE_PROFILES = {
    'intro': {
        'primary': ['kick', 'hat', 'atmos_fx', 'pad', 'bass_loop'],
        'secondary': ['clap', 'synth_loop', 'vocal_shot'],
        'avoid': ['snare_roll', 'fill_fx', 'crash_fx', 'vocal_loop'],
        'intensity': 'low',
    },
    'build': {
        'primary': ['kick', 'hat', 'snare_roll', 'fill_fx', 'synth_loop', 'bass_loop'],
        'secondary': ['clap', 'atmos_fx', 'vocal_shot'],
        'avoid': ['vocal_loop', 'pad'],
        'intensity': 'rising',
    },
    'drop': {
        'primary': ['kick', 'clap', 'hat', 'bass_loop', 'synth_loop', 'vocal_shot'],
        'secondary': ['snare_roll', 'atmos_fx'],
        'avoid': ['pad', 'vocal_loop'],
        'intensity': 'high',
    },
    'break': {
        'primary': ['atmos_fx', 'pad', 'vocal_loop', 'vocal_shot'],
        'secondary': ['hat', 'synth_loop'],
        'avoid': ['kick', 'clap', 'snare_roll'],
        'intensity': 'low',
    },
    'outro': {
        'primary': ['kick', 'hat', 'atmos_fx', 'pad'],
        'secondary': ['clap', 'synth_loop'],
        'avoid': ['snare_roll', 'fill_fx', 'crash_fx', 'vocal_loop'],
        'intensity': 'low',
    }
}
```

**Joint scoring** (NUEVO v0.1.2):
```python
# sample_selector.py:807-820
JOINT_SCORING_GROUPS = {
    'drum_kit': ['kick', 'snare', 'clap', 'hat', 'hat_closed', 'hat_open'],
    'music_group': ['bass_loop', 'synth_loop', 'pad', 'lead', 'chord'],
    'vocal_fx_group': ['vocal_loop', 'vocal_shot', 'atmos_fx', 'fill_fx'],
    'transition_group': ['fill_fx', 'snare_roll', 'crash_fx'],
}

FOLDER_COMPATIBILITY_BONUS = {
    'exact_same': 1.5,
    'same_parent': 1.3,
    'same_grandparent': 1.15,
    'different': 0.85,
}
```

**Estado**: Implementado, necesita prueba en generación real

---

### 4. Groove Extractor - IMPLEMENTADO ✅

**Archivo**: `AbletonMCP_AI/AbletonMCP_AI/MCP_Server/groove_extractor.py` (663 líneas)

**Escaneo recursivo** (v0.1.2):
```python
# groove_extractor.py:65-105
class DembowGrooveExtractor:
    """Extract groove templates from dembow drum loops."""
    
    SCAN_DIRS = ['drumloops', 'perc loop', 'oneshots']
    
    IGNORED_FOLDERS = {
        '.sample_cache', '.segment_rag', '.git', 
        'trash', 'recycle', 'deleted', '__pycache__'
    }
    
    IGNORED_EXTENSIONS = {'.json', '.txt', '.md', '.doc', '.docx'}
    
    def scan_library(self, library_path: str) -> List[str]:
        """Recursively scan for drum loops."""
        audio_files = []
        lib_path = Path(library_path)
        
        for subdir in self.SCAN_DIRS:
            subdir_path = lib_path / subdir
            if not subdir_path.exists():
                continue
            
            # Recursive scan with rglob
            for audio_file in subdir_path.rglob('*.wav'):
                # Skip hidden and ignored
                if any(part.startswith('.') for part in audio_file.parts):
                    continue
                if any(ignored in audio_file.parts for ignored in self.IGNORED_FOLDERS):
                    continue
                
                audio_files.append(str(audio_file))
        
        return audio_files
```

**Estructura de template**:
```python
# groove_extractor.py:40-62
@dataclass
class GrooveTemplate:
    source_file: str
    bpm: float
    kick_positions: List[float]      # 0-4 beats
    snare_positions: List[float]
    hat_positions: List[float]
    kick_velocities: List[float]       # 0.0-1.0
    snare_velocities: List[float]
    hat_velocities: List[float]
    timing_variance_ms: float
    density: float
    style: str = "dembow"
    
    def to_dict(self) -> Dict:
        return {
            'source_file': self.source_file,
            'bpm': self.bpm,
            'kick_positions': self.kick_positions,
            # ... etc
        }
```

**Detección de transientes**:
```python
# audio_analyzer.py:180-220
def _detect_transients_librosa(self, audio: np.ndarray, sr: int) -> np.ndarray:
    """Detect transient positions using librosa onset detection."""
    # Onset envelope
    onset_env = librosa.onset.onset_strength(
        y=audio, 
        sr=sr,
        hop_length=512
    )
    
    # Peak picking
    onset_frames = librosa.util.peak_pick(
        onset_env,
        pre_max=3,
        post_max=3,
        pre_avg=3,
        post_avg=3,
        delta=0.5,
        wait=3
    )
    
    # Convert to timestamps
    onset_times = librosa.frames_to_time(onset_frames, sr=sr, hop_length=512)
    
    # Filter by energy (RMS)
    onset_times = self._filter_by_energy(audio, sr, onset_times)
    
    return onset_times
```

**Resultados**:
- **v0.1.1**: 11 templates (solo drumloops/*.wav)
- **v0.1.2**: 16 templates (76 archivos escaneados recursivamente)
- Cache: `~/.abletonmcp_ai/dembow_groove_templates.json`

**Estado**: Implementado y expandido, probado con librería real

---

### 5. Async Infrastructure - IMPLEMENTADO ⚠️ CON ISSUE

**Archivo**: `AbletonMCP_AI/AbletonMCP_AI/MCP_Server/server.py`

**4 Tools MCP expuestas**:
```python
# server.py:6503-6614

@mcp.tool()
async def generate_track_async(
    genre: str,
    style: str = "",
    bpm: int = 0,
    key: str = "",
    structure: str = "standard"
) -> str:
    """Generate a track asynchronously."""
    job_id = _submit_generation_job(
        job_type="track",
        params={"genre": genre, "style": style, "bpm": bpm, "key": key, "structure": structure}
    )
    return json.dumps({
        "status": "queued",
        "job_id": job_id,
        "message": "Track generation queued"
    })

@mcp.tool()
async def generate_song_async(
    genre: str,
    style: str = "",
    bpm: int = 0,
    key: str = "",
    structure: str = "standard",
    auto_play: bool = True,
    apply_automation: bool = True
) -> str:
    """Generate a full song asynchronously."""
    job_id = _submit_generation_job(
        job_type="song",
        params={...}
    )
    return json.dumps({
        "status": "queued",
        "job_id": job_id,
        "message": "Song generation queued"
    })

@mcp.tool()
async def get_generation_job_status(job_id: str) -> str:
    """Get status of a generation job."""
    with _generation_job_lock:
        job = _generation_jobs.get(job_id)
        if not job:
            return json.dumps({"status": "not_found", "job_id": job_id})
        
        return json.dumps({
            "status": job["status"],
            "job_id": job_id,
            "result": job.get("result"),
            "error": job.get("error"),
            "future_done": job["future"].done() if job.get("future") else False
        })

@mcp.tool()
async def cancel_generation_job(job_id: str) -> str:
    """Cancel a queued or running generation job."""
    with _generation_job_lock:
        job = _generation_jobs.get(job_id)
        if not job:
            return json.dumps({"status": "not_found", "job_id": job_id})
        
        if job["status"] == "queued":
            job["status"] = "cancelled"
            return json.dumps({"status": "cancelled", "job_id": job_id})
        
        return json.dumps({
            "status": "cannot_cancel", 
            "job_id": job_id,
            "current_status": job["status"]
        })
```

**Infrastructure interna**:
```python
# server.py:4734-5101

# Global state
_generation_jobs: Dict[str, Any] = {}
_generation_job_lock = threading.RLock()

# Thread pool for async jobs
_generation_executor = ThreadPoolExecutor(max_workers=2)

def _submit_generation_job(job_type: str, params: Dict) -> str:
    """Submit a generation job to the thread pool."""
    job_id = str(uuid.uuid4())[:12]
    
    with _generation_job_lock:
        _generation_jobs[job_id] = {
            "job_id": job_id,
            "type": job_type,
            "status": "queued",
            "params": params,
            "result": None,
            "error": None,
            "created_at": time.time()
        }
    
    # Submit to thread pool
    future = _generation_executor.submit(_run_generation_job, job_id, job_type, params)
    
    with _generation_job_lock:
        _generation_jobs[job_id]["future"] = future
        _generation_jobs[job_id]["status"] = "running"
    
    return job_id

def _run_generation_job(job_id: str, job_type: str, params: Dict):
    """Actually run the generation job."""
    try:
        if job_type == "track":
            result = _generate_track_internal(params)
        else:
            result = _generate_song_internal(params)
        
        with _generation_job_lock:
            _generation_jobs[job_id]["status"] = "completed"
            _generation_jobs[job_id]["result"] = result
            
    except Exception as e:
        with _generation_job_lock:
            _generation_jobs[job_id]["status"] = "failed"
            _generation_jobs[job_id]["error"] = str(e)
```

**⚠️ ISSUE CRÍTICO ENCONTRADO**:

**Problema**: El servidor MCP se bloquea completamente durante la generación

**Síntomas**:
1. Job se encola correctamente (status: "queued")
2. Job cambia a "running"
3. Servidor deja de responder a cualquier comando MCP
4. `get_generation_job_status` timeout
5. Después de 10+ minutos, servidor crashea

**Logs de error**:
```
MCP error -32001: Request timed out
Connection closed
[WinError 10054] An existing connection was forcibly closed
```

**Causa root**: ThreadPoolExecutor no libera el GIL de Python durante la generación, bloqueando todo el servidor MCP.

**Posibles soluciones**:
1. Usar `multiprocessing.Process` en vez de `ThreadPoolExecutor`
2. Añadir `asyncio` con `run_in_executor` y checkpoints
3. Separar el job runner en proceso independiente con queue
4. Usar `fastapi` o similar para endpoint de status separado

---

### 6. Smoke Test - IMPLEMENTADO ⚠️ CON ISSUE

**Archivo**: `temp\smoke_test_async.py` (547 líneas)

**Estructura**:
```python
class MCPServerClient:
    """Client to invoke MCP tools directly from server.py."""
    
    def __init__(self):
        self.server_module = self._load_server()
    
    def _load_server(self):
        spec = importlib.util.spec_from_file_location(
            "server", 
            r"C:\ProgramData\Ableton\Live 12 Suite\Resources\MIDI Remote Scripts\AbletonMCP_AI\AbletonMCP_AI\MCP_Server\server.py"
        )
        server = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(server)
        return server
    
    async def generate_song_async(self, **kwargs):
        return await self.server_module.generate_song_async(**kwargs)
    
    async def get_generation_job_status(self, job_id):
        return await self.server_module.get_generation_job_status(job_id)

class SmokeTest:
    """End-to-end smoke test for async generation."""
    
    async def run(self):
        # 1. Test connection
        # 2. Launch async job
        # 3. Poll status
        # 4. Verify tracks
        # 5. Check manifest
        pass
```

**Uso**:
```powershell
# Test básico
python temp\smoke_test_async.py

# Con opciones
python temp\smoke_test_async.py --use-track --genre tech-house --poll-interval 2

# Con reporte JSON
python temp\smoke_test_async.py --save-report report.json --json
```

**⚠️ Issue encontrado**: 
El smoke test carga server.py mediante `importlib.util.spec_from_file_location()`, lo que crea una instancia de módulo separada. Esto significa que el diccionario global `_generation_jobs` no es compartido entre la llamada de submit y la de status check.

**Fix necesario**: Usar una sola instancia del cliente MCP o usar el socket directo de Live para status.

---

## 📁 Archivos Tocados

### Archivos Modificados (8):

| Archivo | Líneas | Cambios |
|---------|--------|---------|
| `abletonmcp_init.py` | 47 | Timeout fix para clear_all_tracks, método _clear_all_tracks |
| `sample_selector.py` | ~300 | Same-pack strict, section-aware, joint scoring |
| `pack_brain.py` | ~150 | Folder compatibility methods |
| `groove_extractor.py` | 663 | Nuevo módulo + expansión recursiva |
| `audio_analyzer.py` | 43 | Transient detection para groove |
| `song_generator.py` | 89 | Aplicación de groove en patrones |
| `server.py` | ~200 | 4 tools async, infrastructure |
| `zai_judges.py` | 362 | Nuevo módulo, retry/cache |

### Archivos Creados (3):

| Archivo | Líneas | Propósito |
|---------|--------|-----------|
| `temp\smoke_test_async.py` | 547 | Test suite end-to-end |
| `docs/SPRINT_v0.1.2_CHANGES.md` | 293 | Documentación de realidad |
| `docs/SPRINT_v0.1.1_CHANGES.md` | 297 | Resumen v0.1.1 |

### Archivos de Documentación Actualizados (3):

| Archivo | Cambios |
|---------|---------|
| `KIMI_K2_ACTIVE_HANDOFF.md` | Estado real verificado |
| `docs/SPRINT_v0.1.2_NEXT.md` | Sprint activo actualizado |
| `docs/ROADMAP.md` | Referencia canonical |

---

## ✅ Validaciones Realizadas

### Compilación
```powershell
✅ python -m py_compile "abletonmcp_init.py"
✅ python -m py_compile "AbletonMCP_AI/AbletonMCP_AI/MCP_Server/server.py"
✅ python -m py_compile "AbletonMCP_AI/AbletonMCP_AI/MCP_Server/zai_judges.py"
✅ python -m py_compile "AbletonMCP_AI/AbletonMCP_AI/MCP_Server/sample_selector.py"
✅ python -m py_compile "AbletonMCP_AI/AbletonMCP_AI/MCP_Server/pack_brain.py"
✅ python -m py_compile "AbletonMCP_AI/AbletonMCP_AI/MCP_Server/groove_extractor.py"
✅ python -m py_compile "temp\smoke_test_async.py"
```

### Validación Runtime
| Componente | Estado | Detalle |
|------------|--------|---------|
| clear_all_tracks | ✅ VALIDADO | 3/3 tests pasaron en Live |
| async job queuing | ✅ VALIDADO | Jobs se encolan correctamente |
| async status polling | ⚠️ PARCIAL | Funciona pero server bloquea |
| groove extraction | ✅ VALIDADO | 16 templates de librería real |
| same-pack selection | ⚠️ SIN VALIDAR | Código listo, falta generación real |
| Z.ai retry/cache | ⚠️ SIN VALIDAR | Código listo, falta test con 429 |

---

## ⚠️ Issues Conocidos

### Críticos

1. **Server MCP se bloquea durante generación async**
   - **Impacto**: Clientes no pueden consultar status, timeout
   - **Causa**: ThreadPoolExecutor mantiene GIL
   - **Workaround**: Ninguno, necesita fix
   - **Prioridad**: ALTA

2. **Smoke test module isolation**
   - **Impacto**: "Job not found" en primer poll
   - **Causa**: `_generation_jobs` no compartido entre instancias
   - **Fix**: Usar socket directo o singleton
   - **Prioridad**: MEDIA

3. **BPM detection en loops**
   - **Impacto**: Todos los templates muestran 95.0 BPM
   - **Causa**: librosa clasifica loops como one-shots
   - **Fix**: Mejorar algoritmo o usar metadata
   - **Prioridad**: BAJA

### Importantes

4. **clear_all_tracks error blando**
   - **Impacto**: Mensaje "Couldn't delete track" al final (aunque funciona)
   - **Estado**: Fix de timeout aplicado, error puede persistir en logs
   - **Prioridad**: BAJA

5. **Async generation toma 10+ minutos**
   - **Impacto**: Tests timeout antes de completar
   - **Causa**: Generación heavy + server blocking
   - **Workaround**: Necesita fix del blocking
   - **Prioridad**: ALTA

---

## 🎯 Próximos Pasos Recomendados

### URGENTE - Fix Server Blocking

**Opción A: Multiprocessing** (Recomendado)
```python
# En lugar de ThreadPoolExecutor
from multiprocessing import Process, Queue

def _submit_generation_job(job_type, params):
    job_id = generate_uuid()
    queue = Queue()
    process = Process(
        target=_run_generation_in_process,
        args=(job_id, job_type, params, queue)
    )
    process.start()
    
    # Main process sigue libre para responder MCP
    return job_id
```

**Opción B: Asyncio con checkpoints**
```python
async def _generate_with_checkpoints(params):
    for section in ['intro', 'build', 'drop', 'break', 'outro']:
        await generate_section(section)
        await asyncio.sleep(0.1)  # Yield control
```

**Opción C: Servidor de jobs separado**
- Crear `job_runner.py` como proceso independiente
- Comunicación via socket o archivo
- MCP server solo orquesta, no genera

### Media Prioridad

6. **Validar same-pack selection**
   - Generar track y inspeccionar logs
   - Verificar fill_fx/snare_roll vienen de pack principal

7. **Validar Z.ai retry**
   - Probar contra API real
   - Forzar 429 si es posible (rate limiting)

8. **Fix smoke test**
   - Usar socket directo de Live (127.0.0.1:9877)
   - O mantener singleton del server module

### Baja Prioridad

9. **Mejorar BPM detection**
   - Usar tempo detection más robusto
   - O parsear BPM del filename

10. **Documentar groove templates**
    - Listar todos los templates extraídos
    - Documentar qué loops son mejores

---

## 📊 Métricas Finales

```
Tareas implementadas:     9/10 (90%)
Tareas validadas:         4/10 (40%)
Archivos compilables:     11/11 (100%)
Issues críticos:          1
Issues totales:           5
Líneas de código nuevas:  ~2000
Tests creados:            1 (smoke_test_async.py)
Documentación creada:      3 archivos MD
```

---

## 📚 Referencias

### Entrypoints Críticos
- MCP Server: `AbletonMCP_AI/AbletonMCP_AI/MCP_Server/server.py`
- Runtime Live: `abletonmcp_init.py`
- Wrapper: `mcp_wrapper.py`
- Shim: `AbletonMCP_AI/__init__.py`

### Documentación
- `KIMI_K2_BOOTSTRAP.md` - Orden de lectura para nuevos agentes
- `KIMI_K2_ACTIVE_HANDOFF.md` - Estado actual verificado
- `CLAUDE.md` - Reglas del proyecto
- `docs/ROADMAP.md` - Roadmap canonical
- `docs/SPRINT_v0.1.2_NEXT.md` - Sprint activo
- `docs/KNOWN_ISSUES.md` - Issues conocidos

### Comandos Útiles
```powershell
# Compilar
python -m py_compile "abletonmcp_init.py"

# Ver logs Ableton
Get-Content "$env:APPDATA\Ableton\Live 12.0.15\Preferences\Log.txt" -Tail 100

# Ver puerto
netstat -an | findstr 9877

# Correr smoke test
python temp\smoke_test_async.py --use-track --genre tech-house
```

---

## 📝 Notas para Codex

1. **No confíes ciegamente en docs históricos**: Siempre verificar con código real primero
2. **Separar implementación de validación**: El código puede estar listo pero sin probar en vivo
3. **Server blocking es el issue más crítico**: Arreglar esto primero antes de más features
4. **Usar PowerShell en Windows**: No bash, rutas absolutas Windows
5. **Validar con runtime**: `get_session_info`, `get_tracks`, logs de Ableton
6. **El puerto 9877 escucha**: Pero eso no significa que todo funcione

---

**Documento creado por**: Kimi K2 (opencode)  
**Para**: Codex / Próximo agente  
**Fecha**: 2026-03-30  
**Estado**: Listo para handoff con Reality Check incluido

---

## Reality Check (Added 2026-03-30)

### Claims vs Reality

| Claim | Reality | Status |
|-------|---------|--------|
| "Código implementado 100%" | Code exists but not all wired to real flow | PARTIAL (85% wired) |
| Section-aware selection works | Code exists in `sample_selector.py` but not called from server.py during generation | NOT WIRED |
| Joint scoring (drum kit coherence) | `JOINT_SCORING_GROUPS` defined but selections not recorded, joint scoring not applied | NOT WIRED |
| `record_section_selection` | Method exists but never called | DEAD CODE |
| `section_context` tracking | `SECTION_ROLE_PROFILES` exists but section context never set | NOT WIRED |
| Async jobs work | Infrastructure exists but server blocks during generation | ISSUE FOUND |
| Same-pack strict selection | Code ready but not validated in real generation | UNVALIDATED |
| Z.ai retry/cache | Implemented but not tested against real 429s | UNVALIDATED |
| Groove extractor | Implemented and tested with real library | ✅ WORKS |
| clear_all_tracks | Implemented and validated in Live | ✅ WORKS |

### What's Actually True

- ✅ **clear_all_tracks**: Implemented and validated in Live 3/3 times
- ✅ **Z.ai retry/cache infrastructure**: Implemented with exponential backoff
- ✅ **Groove extractor**: 16 templates extracted from real library
- ✅ **Async job queuing**: Jobs queue correctly
- ⚠️ **Section-aware selection**: Code exists but DEAD (not wired to server.py flow)
- ⚠️ **Joint scoring**: Groups defined but no selection recording → no joint scoring
- ⚠️ **Async status polling**: Infrastructure ready but server blocking prevents status checks
- ❌ **Async completion**: Jobs start but server blocks, causing timeouts

### What Needs Wiring

1. **section_context** needs to be set from server.py during generation
   - Currently `SECTION_ROLE_PROFILES` exists but never used
   - Generation flow doesn't know which section it's in

2. **record_section_selection** needs to be called after each selection
   - Method exists in `sample_selector.py`
   - Never called from generation flow
   - Required for joint scoring to work

3. **joint_scoring** needs selections to be recorded first
   - `JOINT_SCORING_GROUPS` and `FOLDER_COMPATIBILITY_BONUS` defined
   - Can't apply joint scoring without recorded selections

4. **Section-aware filtering** needs to be integrated into selection flow
   - `SECTION_ROLE_PROFILES` defines primary/secondary/avoid per section
   - Not used in actual `select_samples()` call chain

### Honest Assessment

**What works**: Infrastructure, extraction, caching, clearing tracks, compiling  
**What exists but is dead**: Section-aware selection, joint scoring, same-pack strict enforcement  
**What has issues**: Async blocking, smoke test module isolation  
**What's unvalidated**: Same-pack selection, Z.ai 429 handling  

**Bottom line**: ~40% of features are runtime validated, ~45% exist but aren't wired, ~15% needs fixing.
