Rememberizer Vector Store MCP Server
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Rememberizer Vector Store MCP Serversearch for recent AI research papers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Rememberizer Vector Store MCP Server
A Model Context Protocol server for LLMs to interact with Rememberizer Vector Store.
Components
Resources
The server provides access to your Vector Store's documents in Rememberizer.
Tools
rememberizer_vectordb_searchSearch for documents in your Vector Store by semantic similarity
Input:
q(string): Up to a 400-word sentence to find semantically similar chunks of knowledgen(integer, optional): Number of similar documents to return (default: 5)
rememberizer_vectordb_agentic_searchSearch for documents in your Vector Store by semantic similarity with LLM Agents augmentation
Input:
query(string): Up to a 400-word sentence to find semantically similar chunks of knowledge. This query can be augmented by our LLM Agents for better results.n_chunks(integer, optional): Number of similar documents to return (default: 5)user_context(string, optional): The additional context for the query. You might need to summarize the conversation up to this point for better context-awared results (default: None)
rememberizer_vectordb_list_documentsRetrieves a paginated list of all documents
Input:
page(integer, optional): Page number for pagination, starts at 1 (default: 1)page_size(integer, optional): Number of documents per page, range 1-1000 (default: 100)
Returns: List of documents
rememberizer_vectordb_informationGet information of your Vector Store
Input: None required
Returns: Vector Store information details
rememberizer_vectordb_create_documentCreate a new document for your Vector Store
Input:
text(string): The content of the documentdocument_name(integer, optional): A name for the document
rememberizer_vectordb_delete_documentDelete a document from your Vector Store
Input:
document_id(integer): The ID of the document you want to delete
rememberizer_vectordb_modify_documentChange the name of your Vector Store document
Input:
document_id(integer): The ID of the document you want to modify
Related MCP server: Rememberizer MCP Server
Installation
Manual Installation: Use uvx command to install the Rememberizer Vector Store MCP Server.
uvx mcp-rememberizer-vectordbVia MseeP AI Helper App: If you have MseeP AI Helper app installed, you can search for "Rememberizer VectorDb" and install the mcp-rememberizer-vectordb.
Configuration
Environment Variables
The following environment variables are required:
REMEMBERIZER_VECTOR_STORE_API_KEY: Your Rememberizer Vector Store API token
You can register an API key by create your own Vector Store in Rememberizer.
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
"mcpServers": {
"rememberizer": {
"command": "uvx",
"args": ["mcp-rememberizer-vectordb"],
"env": {
"REMEMBERIZER_VECTOR_STORE_API_KEY": "your_rememberizer_api_token"
}
},
}Usage with MseeP AI Helper App
Add the env REMEMBERIZER_VECTOR_STORE_API_KEY to mcp-rememberizer-vectordb.
License
This MCP server is licensed under the Apache License 2.0.
This server cannot be deployed
Maintenance
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