Youtube Video Analyzer vs Nanobanana MCP Server
A side-by-side look at the Youtube Video Analyzer and Nanobanana MCP Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Youtube Video Analyzer for multimedia process work as a local process; choose Nanobanana MCP Server 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 Youtube Video Analyzer 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. - You want tools like
analyze_youtube_video, analyze_long_youtube_video, continue_long_video_analysis.
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.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Multimedia Process | Multimedia Process |
| Quality signal | 55/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | GEMINI_API_KEY | GEMINI_API_KEYNANOBANANA_AUTH_METHODGCP_PROJECT_IDGCP_REGION |
| Verified owner | Community listing | Community listing |
| GitHub stars | 5 | 400 |
| Directory activity | 2 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 3 | 4 |
| Full listing | View Youtube Video Analyzer | View Nanobanana MCP Server |
Tools & capabilities
Youtube Video Analyzer tools 3
analyze_youtube_videoAnalyze a public YouTube video with Google Gemini. The YouTube URL is normalized to a canonical watch URL, then sent as video input with optional clip offsets attached through videoMetadata. Detects the dominant language of the video, returns natural-language fields in that language, and accepts an optional custom JSON schema.analyze_long_youtube_videoAnalyze a long public YouTube video with Gemini long-video handling. Auto mode prefers uploaded-file analysis first because Files API is the recommended path for long videos, and falls back to URL chunks when needed. Direct YouTube URL chunking remains available as an explicit strategy for public videos, but should be treated as a preview-oriented convenience path.continue_long_video_analysisContinue analyzing a previously uploaded long-video session. When possible, this tool reuses the cached uploaded asset created by analyze_long_youtube_video instead of re-downloading the video.
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
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.
Youtube Video Analyzer
{
"mcpServers": {
"youtube-video-analyzer": {
"command": "npx",
"args": [
"-y",
"@ludylops/youtube-video-analyzer-mcp"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY_HERE"
}
}
}
}
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"
}
}
}
}
Frequently asked questions
Youtube Video Analyzer is categorized under Multimedia Process and uses a local stdio subprocess. Nanobanana MCP Server belongs to Multimedia Process and uses a local stdio subprocess. Pick Youtube Video Analyzer for multimedia process work and Nanobanana MCP Server 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.
Youtube Video Analyzer has no declared pricing — check the project's own site. Nanobanana MCP Server has no declared pricing — check the project's own site. 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 youtube-video-analyzer and nanobanana-mcp-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Youtube Video Analyzer has a quality signal of 55/100 (Good) with 2 views, 0 installs and 5 GitHub stars. Nanobanana MCP Server has 63/100 (Good) with 1 views, 0 installs and 400 GitHub stars.