Rememberizer MCP Server

[](https://mseep.ai/app/skydeckai-mcp-server-rememberizer) # MCP Server Rememberizer [](https://mseep.ai/app/fe7a40fd-30c1-4767-84f9-d33bf997497e) A [Model Context Protocol](https://www.anthropic.com/news/model-context-protocol) server for interacting with Rememberizer's document and knowledge management API. This server enables Large Language Models to search, retrieve, and manage documents and integrations through Rememberizer. Please note that `mcp-server-rememberizer` is currently in development and the functionality may be subject to change. ## Components ### Resources The server provides access to two types of resources: Documents or Slack discussions ### Tools 1. `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 - Returns: Search results as text output 2. `smart_search_internal_knowledge` - 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 - Returns: Search results as text output 3. `list_internal_knowledge_systems` - 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 4. `rememberizer_account_information` - 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 5. `list_personal_team_knowledge_documents` - 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) - Returns: List of documents 6. `remember_this` - 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 - Returns: Confirmation data ## Installation ### Manual Installation ```bash uvx mcp-server-rememberizer ``` ### Via MseeP AI Helper App If you have MseeP AI Helper app installed, you can search for "Rememberizer" and install the mcp-server-rememberizer.  ## Configuration ### Environment Variables The following environment variables are required: - `REMEMBERIZER_API_TOKEN`: Your Rememberizer API token You can register an API key by creating your own [Common Knowledge in Rememberizer](https://docs.rememberizer.ai/developer/registering-and-using-api-keys). ### Usage with Claude Desktop Add this to your `claude_desktop_config.json`: ```json "mcpServers": { "rememberizer": { "command": "uvx", "args": ["mcp-server-rememberizer"], "env": { "REMEMBERIZER_API_TOKEN": "your_rememberizer_api_token" } }, } ``` ### Usage with MseeP AI Helper App Add the env REMEMBERIZER_API_TOKEN to mcp-server-rememberizer.  With support from the Rememberizer MCP server, you can now ask the following questions in your Claude Desktop app or SkyDeck AI GenStudio - _What is my Rememberizer account?_ - _List all documents that I have there._ - _Give me a quick summary about "..."_ - and so on... To learn more about Rememberizer MCP Server: https://docs.rememberizer.ai/personal-use/integrations/rememberizer-mcp-servers ## License This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.