Rememberizer MCP Server for Common Knowledge

Access personal and team knowledge repositories, including documents and Slack discussions.

Rememberizer MCP Server for Common Knowledge

Please note thatrememberizer-mcp-test-ckis currently in development and the functionality may be subject to change.

The server provides access to two types of resources: Documents or Slack discussions
-

retrieve_semantically_similar_internal_knowledge

- Send a block of text and retrieve cosine similar matches from your connected Rememberizer personal/team internal knowledge and memory repository
- Input:

- match_this(string): Up to a 400-word sentence for which you wish to find semantically similar chunks of knowledge
- n_results(integer, optional): Number of semantically similar chunks of text to return. Use 'n_results=3' for up to 5, and 'n_results=10' for more information
- from_datetime_ISO8601(string, optional): Start date in ISO 8601 format with timezone (e.g., 2023-01-01T00:00:00Z). Use this to filter results from a specific date
- to_datetime_ISO8601(string, optional): End date in ISO 8601 format with timezone (e.g., 2024-01-01T00:00:00Z). Use this to filter results until a specific date

- Search for documents in Rememberizer in its personal/team internal knowledge and memory repository using a simple query that returns the results of an agentic search. The search may include sources such as Slack discussions, Gmail, Dropbox documents, Google Drive documents, and uploaded files
- Input:

- query(string): Up to a 400-word sentence for which you wish to find semantically similar chunks of knowledge
- 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
- n_results(integer, optional): Number of semantically similar chunks of text to return. Use 'n_results=3' for up to 5, and 'n_results=10' for more information
- from_datetime_ISO8601(string, optional): Start date in ISO 8601 format with timezone (e.g., 2023-01-01T00:00:00Z). Use this to filter results from a specific date
- to_datetime_ISO8601(string, optional): End date in ISO 8601 format with timezone (e.g., 2024-01-01T00:00:00Z). Use this to filter results until a specific date

- List the sources of personal/team internal knowledge. These may include Slack discussions, Gmail, Dropbox documents, Google Drive documents, and uploaded files
- Input: None required
- Returns: List of available integrations

- Get information about your Rememberizer.ai personal/team knowledge repository account. This includes account holder name and email address
- Input: None required
- Returns: Account information details

- Retrieves a paginated list of all documents in your personal/team knowledge system. Sources could include Slack discussions, Gmail, Dropbox documents, Google Drive documents, and uploaded files
- 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)

- Save a piece of text information in your Rememberizer.ai knowledge system so that it may be recalled in future through tools retrieve_semantically_similar_internal_knowledge or smart_search_internal_knowledge
- Input:

- name(string): Name of the information. This is used to identify the information in the future
- content(string): The information you wish to memorize

If you have SkyDeck AI Helper app installed, you can search for "Rememberizer" and install the rememberizer-mcp-test-ck.

Add this to yourclaude_desktop_config.json:

"mcpServers": { "rememberizer": { "command": "uvx", "args": ["rememberizer-mcp-test-ck"] }, }

With support from the Rememberizer MCP server for Common Knowledge, you can now ask the following questions in your Claude Desktop app or SkyDeck AI GenStudio

Give me a quick summary about "..."

This project is licensed under the Apache License 2.0 - see theLICENSEfile for details.

Provides AI assistants with intelligent access to ML textbook content for creating accurate, source-grounded documentation.