docs: Remove obsolete volume_mode references from documentation

The volume_mode parameter is no longer used since workflows now upload files to MinIO storage instead of mounting volumes directly. This commit removes all references to volume_mode from:

- Backend API documentation (README.md)
- Tutorial getting started guide
- MCP integration guide
- CLI AI reference documentation
- SDK documentation and examples
- Test project documentation

All curl examples and code samples have been updated to reflect the current MinIO-based file upload approach.

backend/README.md

@@ -88,10 +88,6 @@ tags:
   - "analysis"
   - "comprehensive"
 
-supported_volume_modes:
-  - "ro"
-  - "rw"
-
 requirements:
   tools:
     - "file_scanner"
@@ -111,11 +107,6 @@ parameters:
       type: string
       default: "/workspace"
       description: "Path to analyze"
-    volume_mode:
-      type: string
-      enum: ["ro", "rw"]
-      default: "ro"
-      description: "Volume mount mode"
     scanner_config:
       type: object
       description: "Scanner configuration"
@@ -160,7 +151,6 @@ curl -X POST "http://localhost:8000/workflows/security_assessment/submit" \
   -H "Content-Type: application/json" \
   -d '{
     "target_path": "/tmp/project",
-    "volume_mode": "ro",
     "resource_limits": {
       "memory_limit": "1Gi",
       "cpu_limit": "1"
@@ -183,7 +173,6 @@ Content-Type: multipart/form-data
 Parameters:
   file: File upload (supports .tar.gz for directories)
   parameters: JSON string of workflow parameters (optional)
-  volume_mode: "ro" or "rw" (default: "ro")
   timeout: Execution timeout in seconds (optional)
 ```
 
@@ -194,13 +183,11 @@ Example using curl:
 tar -czf project.tar.gz /path/to/project
 curl -X POST "http://localhost:8000/workflows/security_assessment/upload-and-submit" \
   -F "file=@project.tar.gz" \
-  -F "parameters={\"check_secrets\":true}" \
-  -F "volume_mode=ro"
+  -F "parameters={\"check_secrets\":true}"
 
 # Upload a single file
 curl -X POST "http://localhost:8000/workflows/security_assessment/upload-and-submit" \
-  -F "file=@binary.elf" \
-  -F "volume_mode=ro"
+  -F "file=@binary.elf"
 ```
 
 ### Storage Flow
@@ -257,8 +244,7 @@ class MyModule(BaseModule):
 tar -czf project.tar.gz /home/user/project
 curl -X POST "http://localhost:8000/workflows/security_assessment/upload-and-submit" \
   -F "file=@project.tar.gz" \
-  -F "parameters={\"scanner_config\":{\"patterns\":[\"*.py\"]},\"analyzer_config\":{\"check_secrets\":true}}" \
-  -F "volume_mode=ro"
+  -F "parameters={\"scanner_config\":{\"patterns\":[\"*.py\"]},\"analyzer_config\":{\"check_secrets\":true}}"
 ```
 
 ### Legacy Path-Based Submission
@@ -269,7 +255,6 @@ curl -X POST "http://localhost:8000/workflows/security_assessment/submit" \
   -H "Content-Type: application/json" \
   -d '{
     "target_path": "/home/user/project",
-    "volume_mode": "ro",
     "parameters": {
       "scanner_config": {"patterns": ["*.py"]},
       "analyzer_config": {"check_secrets": true}

docs/docs/how-to/mcp-integration.md

@@ -83,7 +83,6 @@ You should see status responses and endpoint listings.
   "parameters": {
     "workflow_name": "infrastructure_scan",
     "target_path": "/path/to/your/project",
-    "volume_mode": "ro",
     "parameters": {
       "checkov_config": {
         "severity": ["HIGH", "MEDIUM", "LOW"]
@@ -183,7 +182,7 @@ curl http://localhost:8000/workflows/
 ```bash
 curl -X POST http://localhost:8000/workflows/infrastructure_scan/submit \
   -H "Content-Type: application/json" \
-  -d '{"target_path": "/your/path", "volume_mode": "ro"}'
+  -d '{"target_path": "/your/path"}'
 ```
 
 ### General Support

docs/docs/reference/cli-ai.md

@@ -136,7 +136,6 @@ FuzzForge supports the Model Context Protocol (MCP), allowing LLM clients and AI
   "parameters": {
     "workflow_name": "infrastructure_scan",
     "target_path": "/path/to/your/project",
-    "volume_mode": "ro",
     "parameters": {
       "checkov_config": {
         "severity": ["HIGH", "MEDIUM", "LOW"]
@@ -193,7 +192,7 @@ FuzzForge supports the Model Context Protocol (MCP), allowing LLM clients and AI
   `curl http://localhost:8000/workflows/`
 
 - **Scan Submission Errors:**
-  `curl -X POST http://localhost:8000/workflows/infrastructure_scan/submit -H "Content-Type: application/json" -d '{"target_path": "/your/path", "volume_mode": "ro"}'`
+  `curl -X POST http://localhost:8000/workflows/infrastructure_scan/submit -H "Content-Type: application/json" -d '{"target_path": "/your/path"}'`
 
 - **General Support:**
   - Check Docker Compose logs: `docker compose logs fuzzforge-backend`

docs/docs/tutorial/getting-started.md

@@ -186,8 +186,7 @@ For local files, you can use the upload endpoint:
 # Create tarball and upload
 tar -czf project.tar.gz /path/to/your/project
 curl -X POST "http://localhost:8000/workflows/security_assessment/upload-and-submit" \
-     -F "file=@project.tar.gz" \
-     -F "volume_mode=ro"
+     -F "file=@project.tar.gz"
 
 # Check status
 curl "http://localhost:8000/runs/{run-id}/status"

sdk/README.md

@@ -45,7 +45,6 @@ target_path = Path("/path/to/your/project")
 response = client.submit_workflow_with_upload(
     workflow_name="security_assessment",
     target_path=target_path,
-    volume_mode="ro",
     timeout=300
 )
 
@@ -74,7 +73,6 @@ client = FuzzForgeClient(base_url="http://localhost:8000")
 # Submit a workflow with path (only works if backend can access the path)
 submission = create_workflow_submission(
     target_path="/path/on/backend/filesystem",
-    volume_mode="ro",
     timeout=300
 )
 
@@ -103,7 +101,6 @@ def submit_workflow_with_upload(
     workflow_name: str,
     target_path: Union[str, Path],
     parameters: Optional[Dict[str, Any]] = None,
-    volume_mode: str = "ro",
     timeout: Optional[int] = None,
     progress_callback: Optional[Callable[[int, int], None]] = None
 ) -> RunSubmissionResponse:
@@ -114,7 +111,6 @@ def submit_workflow_with_upload(
         workflow_name: Name of the workflow to execute
         target_path: Path to file or directory to upload
         parameters: Optional workflow parameters
-        volume_mode: Volume mount mode ('ro' or 'rw')
         timeout: Optional execution timeout in seconds
         progress_callback: Optional callback(bytes_sent, total_bytes)
 
@@ -144,7 +140,6 @@ response = client.submit_workflow_with_upload(
     workflow_name="security_assessment",
     target_path=Path("./my-project"),
     parameters={"check_secrets": True},
-    volume_mode="ro",
     progress_callback=upload_progress
 )
 

test_projects/README.md

@@ -47,24 +47,21 @@ The vulnerable application can be tested with multiple security workflows:
 curl -X POST http://localhost:8000/workflows/security_assessment/submit \
   -H "Content-Type: application/json" \
   -d '{
-    "target_path": "/path/to/test_projects/vulnerable_app",
-    "volume_mode": "ro"
+    "target_path": "/path/to/test_projects/vulnerable_app"
   }'
 
 # Test Gitleaks secret detection workflow
 curl -X POST http://localhost:8000/workflows/gitleaks_detection/submit \
   -H "Content-Type: application/json" \
   -d '{
-    "target_path": "/path/to/test_projects/vulnerable_app",
-    "volume_mode": "ro"
+    "target_path": "/path/to/test_projects/vulnerable_app"
   }'
 
 # Test TruffleHog secret detection workflow
 curl -X POST http://localhost:8000/workflows/trufflehog_detection/submit \
   -H "Content-Type: application/json" \
   -d '{
-    "target_path": "/path/to/test_projects/vulnerable_app",
-    "volume_mode": "ro"
+    "target_path": "/path/to/test_projects/vulnerable_app"
   }'
 ```
 

test_projects/vulnerable_app/README.md

@@ -82,7 +82,6 @@ curl -X POST "http://localhost:8000/workflows/security_assessment/submit" \
   -H "Content-Type: application/json" \
   -d '{
     "target_path": "/path/to/test_projects/vulnerable_app",
-    "volume_mode": "ro",
     "parameters": {
       "scanner_config": {"check_sensitive": true},
       "analyzer_config": {"check_secrets": true, "check_sql": true}