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_search- Search 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_search- Search 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_documents- Retrieves 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_information- Get information of your Vector Store
- Input: None required
- Returns: Vector Store information details
-
rememberizer_vectordb_create_document- Create 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_document- Delete a document from your Vector Store
- Input:
document_id(integer): The ID of the document you want to delete
-
rememberizer_vectordb_modify_document- Change the name of your Vector Store document
- Input:
document_id(integer): The ID of the document you want to modify
Installation
Manual Installation: Use uvx command to install the Rememberizer Vector Store MCP Server.
uvx mcp-rememberizer-vectordb
Via 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.
Metadata
Release files for mcp-rememberizer-vectordb 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mcp_rememberizer_vectordb-0.1.3.tar.gz | 11.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mcp_rememberizer_vectordb-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 22.0 kB
Release files / mcp_rememberizer_vectordb-0.1.3.tar.gz
| Download URL | mcp_rememberizer_vectordb-0.1.3.tar.gz |
|---|---|
| Size | 11.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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twine/6.1.0 CPython/3.13.0
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Release files / mcp_rememberizer_vectordb-0.1.3-py3-none-any.whl
| Download URL | mcp_rememberizer_vectordb-0.1.3-py3-none-any.whl |
|---|---|
| Size | 11.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.0
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