PicoBerry vs Andrea9293 MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
PicoBerry vs Andrea9293 MCP
In-depth architectural comparison of the PicoBerry and Andrea9293 MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
PicoBerry
Developer Tools · Local stdio
Quality: 51/100 (Good) | Auth: No auth required
Andrea9293 MCP
Developer Tools · Local stdio
Quality: 61/100 (Good) | Auth: No auth required
Verdict Summary: Choose PicoBerry if you need specialized Developer Tools tools running via a local process. Choose Andrea9293 MCP if your workspace requires Developer Tools integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose PicoBerry when:
You need dedicated capabilities in the Developer Tools domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
PicoBerry is categorized under Developer Tools and uses a local stdio subprocess. In contrast, Andrea9293 MCP belongs to Developer Tools using local stdio subprocess. Select PicoBerry when you need capabilities focused on developer tools and Andrea9293 MCP when you require tools for developer tools.
Decompose one image into an exploded parts-board image (server-fixed engine). Input `asset_id`, `image_url`, or local `image_path`; feed the result to `generate_3d_from_image` for a parts-separated mesh.
remesh
Retopologize an existing 3D asset → new asset.
texture
Re-texture (PBR) an existing 3D asset → new asset.
animate
Auto-rig + animate an existing 3D character → new asset.
get_asset
Status + result URLs for one asset.
wait_for_asset
Poll until an asset finishes (or times out), then return it.
+2 more tools listed on main page
Andrea9293 MCP Tools (12)
add_document
Add a document (title, content, optional metadata)
list_documents
List all documents with metadata and content preview
get_document
Retrieve the full content of a document by ID
delete_document
Remove a document, its chunks, database entries, and associated files
process_uploads
Process all files in the uploads folder (chunking + embeddings)
get_uploads_path
Returns the absolute path to the uploads folder
list_uploads_files
Lists files in the uploads folder with size and format info
get_ui_url
Returns the Web UI URL (e.g. http://localhost:3080) — useful to open the dashboard or to locate the uploads folder from the browser
search_documents
Semantic vector search within a specific document
search_all_documents
Hybrid (full-text + vector) cross-document search
get_context_window
Returns a window of chunks around a given chunk index
search_documents_with_ai
🤖 AI-powered search using Gemini (requires `GEMINI_API_KEY`)