Address copilot feedback

Author: pamelafox

.mcp.json

@@ -6,7 +6,7 @@
         },
         "image-search-azure": {
             "type": "http",
-            "url": "https://<your-mcp-container-app>/mcp"
+            "url": "https://ca-mcp-dzwimtrf5gzou.orangeriver-094d6c5a.westus.azurecontainerapps.io/mcp"
         }
     }
 }

README.md

@@ -100,30 +100,44 @@ When `setup_search_service.py` runs (automatically during `azd up`, or manually)
 1. **Blob upload** – Images from the `pictures/` folder are uploaded to an Azure Blob Storage container. Only files placed directly under `pictures/` are uploaded.
 2. **Data source** – A Search data source is configured to point at that blob container.
 3. **Skillset** – Two skills are applied to each image:
-   - [`VisionVectorizeSkill`](https://learn.microsoft.com/azure/search/cognitive-search-skill-vision-vectorize) generates a 1024-dimensional multimodal embedding using Azure AI Vision.
-   - [`ChatCompletionSkill`](https://learn.microsoft.com/azure/search/chat-completion-skill-example-usage) (optional) produces a natural-language description of each image, stored as `verbalized_image`.
+   * [`VisionVectorizeSkill`](https://learn.microsoft.com/azure/search/cognitive-search-skill-vision-vectorize) generates a 1024-dimensional multimodal embedding using Azure AI Vision.
+   * [`ChatCompletionSkill`](https://learn.microsoft.com/azure/search/chat-completion-skill-example-usage) (optional) produces a natural-language description of each image, stored as `verbalized_image`.
 4. **Index** – Results are written to a Search index with an `embedding` vector field backed by an HNSW algorithm, plus a built-in AI Vision vectorizer so that query text is embedded at search time without extra code.
 5. **Indexer** – Runs the pipeline: blob → normalized images → skills → index projections. Each image within a blob becomes its own indexed document.
 
 At query time, the search service vectorizes the text query using the same AI Vision model and performs an approximate nearest-neighbor search over the stored embeddings.
 
-## Running locally
+## Testing deployed endpoints
 
-You can only run locally **after** having successfully run the `azd up` command. If you haven't yet, follow the steps in [Azure deployment](#azure-deployment) above.
+After running `azd up`, you can test both deployed Container Apps.
 
-### Web app
+### Deployed web app
 
-1. Run `azd auth login`
-2. Change dir to `app` and run `./start.ps1` or `./start.sh` depending on your OS.
-3. Open a browser and navigate to `http://localhost:50505`
+1. Run `azd env get-value SERVICE_ACA_URI` to get the deployed web app URL.
+2. Open the URL in a browser.
 
-### MCP server
+### Deployed MCP server
 
-1. Run `azd auth login`
-2. Run `python app/backend/mcp_server.py`
-3. The server starts on `http://localhost:8001`. Add it to your MCP client configuration (e.g. VS Code or Claude Desktop) pointing at that URL.
+1. Run `azd env get-value SERVICE_MCP_URI` to get the deployed MCP server URL.
+2. Append `/mcp` to the URL.
+3. In `.mcp.json`, replace the `url` for the `image-search-azure` entry under `mcpServers` with the resulting URL.
+4. Open your MCP client and use the `image-search-azure` server.
+
+## Testing local endpoints
+
+You can only run the endpoints locally **after** successfully running `azd up`. If you haven't yet, follow the steps in [Azure deployment](#azure-deployment) above.
+
+### Local web app
+
+1. Run `azd auth login`.
+2. Change to the `app` directory and run `./start.ps1` or `./start.sh`, depending on your OS.
+3. Open `http://localhost:50505` in a browser.
+
+### Local MCP server
 
-To connect to a deployed MCP server instead, update `.vscode/mcp.json` with your deployed Container App hostname and append `/mcp` (for example, using the `SERVICE_MCP_URI` output from `azd up`).
+1. Run `azd auth login`.
+2. Run `python app/backend/mcp_server.py`.
+3. Open your MCP client and use the `image-search-local` server from `.mcp.json`, which connects to `http://localhost:8001/mcp`.
 
 ## Adding new images
 

app/backend/image-viewer.html

@@ -136,9 +136,9 @@ <h2 id="title"></h2>
             frame.innerHTML = "";
             const el = document.createElement("img");
             el.src = `data:${img.mimeType || "image/jpeg"};base64,${img.data}`;
-            el.alt = "Blob image";
+            el.alt = img.filename || "Image";
             frame.appendChild(el);
-            title.textContent = img.title || img.filename || "Image";
+            title.textContent = img.filename || "Image";
             description.textContent = img.description || "";
             description.hidden = !img.description;
             metadata.textContent = [

app/backend/mcp_server.py

@@ -171,12 +171,6 @@ def get_image_mime_type(filename: str) -> str:
     return "image/jpeg"
 
 
-def get_image_title(filename: str) -> str:
-    """Create a readable image title from a blob filename."""
-    stem = Path(unquote(filename)).stem
-    return " ".join(stem.replace("_", " ").replace("-", " ").split()).title()
-
-
 THUMBNAIL_SIZE = (256, 256)
 
 
@@ -214,11 +208,13 @@ async def display_image_files(
         "Optional image descriptions from image_search, in the same order as filenames.",
     ] = None,
 ) -> ToolResult:
-    """Fetch images by filename and render them in a carousel with readable titles, descriptions, and file details."""
+    """Fetch images by filename and render them in a carousel with descriptions and file details."""
     if len(filenames) < 1:
         raise ValueError("Provide at least one filename.")
     if descriptions is not None and len(descriptions) != len(filenames):
-        raise ValueError("Descriptions must have the same number of items as filenames.")
+        raise ValueError(
+            "Descriptions must have the same number of items as filenames."
+        )
 
     blob_service_client = get_blob_service_client()
 
@@ -250,7 +246,6 @@ async def display_image_files(
         image_results.append(
             {
                 "filename": filename,
-                "title": get_image_title(filename),
                 "description": descriptions[image_index] if descriptions else "",
                 "mimeType": mime_type,
                 "width": width,

app/frontend/tsconfig.json

@@ -5,12 +5,11 @@
     "lib": ["DOM", "DOM.Iterable", "ESNext"],
     "allowJs": false,
     "skipLibCheck": true,
-    "esModuleInterop": false,
     "allowSyntheticDefaultImports": true,
     "strict": true,
     "forceConsistentCasingInFileNames": true,
     "module": "ESNext",
-    "moduleResolution": "Node",
+    "moduleResolution": "Bundler",
     "resolveJsonModule": true,
     "isolatedModules": true,
     "noEmit": true,