Nanobanana MCP Server vs AI Vision MCP
A side-by-side look at the Nanobanana MCP Server and AI Vision MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Nanobanana MCP Server for multimedia process work as a local process; choose AI Vision MCP for multimedia process as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Nanobanana MCP Server when
- You need dedicated capabilities in the Multimedia Process domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
GEMINI_API_KEY, NANOBANANA_AUTH_METHOD, GCP_PROJECT_ID, GCP_REGION. - You want tools like
generate_image, upload_file, show_output_stats.
Choose AI Vision MCP when
- You need dedicated capabilities in the Multimedia Process domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
IMAGE_PROVIDER, VIDEO_PROVIDER, GEMINI_API_KEY, VERTEX_CLIENT_EMAIL, VERTEX_PRIVATE_KEY, VERTEX_PROJECT_ID, GCS_BUCKET_NAME. - You want tools like
analyze_image, compare_images, detect_objects_in_image.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Multimedia Process | Multimedia Process |
| Quality signal | 63/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | GEMINI_API_KEYNANOBANANA_AUTH_METHODGCP_PROJECT_IDGCP_REGION | IMAGE_PROVIDERVIDEO_PROVIDERGEMINI_API_KEYVERTEX_CLIENT_EMAILVERTEX_PRIVATE_KEYVERTEX_PROJECT_IDGCS_BUCKET_NAME |
| Verified owner | Community listing | Community listing |
| GitHub stars | 400 | 80 |
| Directory activity | 1 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | 4 | 4 |
| Full listing | View Nanobanana MCP Server | View AI Vision MCP |
Tools & capabilities
Nanobanana MCP Server tools 4
generate_imageGenerate new images or edit existing images using natural language instructions. Supports multiple input modes: 1. Pure generation: Just provide a prompt to create new images 2. Multi-image conditioning: Provide up to 3 input images using input_image_path_1/2/3 parameters 3. File ID editing: Edit previously uploaded images using Files API ID 4. File path editing: Edit local images by providing single input image path Automatically detects mode based on parameters or can be explicitly controlled. Input images are read from the local filesystem to avoid massive token usage. Returns both MCP image content blocks and structured JSON with metadata.upload_fileUpload a local file through the Gemini Files API and return its URI & metadata. Useful when the image is larger than 20MB or reused across prompts.show_output_statsShow statistics about the output directory and recently generated images.maintenancePerform maintenance operations following workflows.md patterns. Available operations: - cleanup_expired: Remove expired Files API entries from database - cleanup_local: Clean old local files based on age/LRU - check_quota: Check Files API storage usage vs. ~20GB budget - database_hygiene: Clean up database inconsistencies - full_cleanup: Run all cleanup operations in sequence
AI Vision MCP tools 4
analyze_imageAnalyze an image using AI vision models. Supports URLs, base64 data, and local file paths.compare_imagesCompare multiple images using AI vision models. Supports URLs, base64 data, and local file paths.detect_objects_in_imageDetect objects in an image using AI vision models and generate annotated images with bounding boxes. Supports URLs, base64 data, and local file paths. File handling: explicit filePath → exact path, otherwise → temp directory. Uses optimized default parameters for object detection.analyze_videoAnalyze a video using AI vision models. Supports URLs and local file paths.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Nanobanana MCP Server
{
"mcpServers": {
"nanobanana-mcp-server": {
"command": "uvx",
"args": [
"nanobanana-mcp-server@latest"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY_HERE",
"NANOBANANA_AUTH_METHOD": "YOUR_NANOBANANA_AUTH_METHOD_HERE",
"GCP_PROJECT_ID": "YOUR_GCP_PROJECT_ID_HERE",
"GCP_REGION": "YOUR_GCP_REGION_HERE"
}
}
}
}
AI Vision MCP
{
"mcpServers": {
"tan-yong-sheng-ai-vision-mcp": {
"command": "npx",
"args": [
"-y",
"ai-vision-mcp"
],
"env": {
"IMAGE_PROVIDER": "YOUR_IMAGE_PROVIDER_HERE",
"VIDEO_PROVIDER": "YOUR_VIDEO_PROVIDER_HERE",
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY_HERE",
"VERTEX_CLIENT_EMAIL": "YOUR_VERTEX_CLIENT_EMAIL_HERE",
"VERTEX_PRIVATE_KEY": "YOUR_VERTEX_PRIVATE_KEY_HERE",
"VERTEX_PROJECT_ID": "YOUR_VERTEX_PROJECT_ID_HERE",
"GCS_BUCKET_NAME": "YOUR_GCS_BUCKET_NAME_HERE"
}
}
}
}
Frequently asked questions
Nanobanana MCP Server is categorized under Multimedia Process and uses a local stdio subprocess. AI Vision MCP belongs to Multimedia Process and uses a local stdio subprocess. Pick Nanobanana MCP Server for multimedia process work and AI Vision MCP for multimedia process.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Nanobanana MCP Server has no declared pricing — check the project's own site. AI Vision MCP is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add nanobanana-mcp-server and tan-yong-sheng-ai-vision-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Nanobanana MCP Server has a quality signal of 63/100 (Good) with 1 views, 0 installs and 400 GitHub stars. AI Vision MCP has 63/100 (Good) with 5 views, 0 installs and 80 GitHub stars.