mcp -rememberizer

The MCP Rememberizer server exposes a set of tools that let large language models search, retrieve, and manage documents and knowledge stored in Rememberizer. It provides access to two resource types: Documents and Slack discussions, and offers tools for semantic search across internal knowledge, as well as listing and managing Rememberizer content. The key tools are: 1) retrieve_semantically_similar_internal_knowledge, which accepts a block of text and returns cosine-similar knowledge chunks from your Rememberizer repository; 2) smart_search_internal_knowledge, which performs an agentic search across multiple sources (Slack, Gmail, Google Drive, Dropbox, uploaded files) and returns results for a given query; 3) list_internal_knowledge_systems, which lists available integrations for your Rememberizer account; 4) rememberizer_account_information, which retrieves account holder name and email; 5) list_personal_team_knowledge_documents, which returns a paginated list of documents; and 6) remember_this, which saves a piece of text to Rememberizer for future recall. To use these tools, you must have your Rememberizer API token configured as an environment variable (REMEMBERIZER_API_TOKEN). The server can be deployed through uvx as shown, and then invoked by your Claude Desktop or MSEE P AI Helper workflow when configured with the token. This setup enables remember-based querying, quick summaries, and document discovery within your integrated Rememberizer knowledge base.

Prerequisites:

• Python with uvw/uvx installed (as per Rememberizer MCP server instructions).
• Access to a Rememberizer account and an API token (REMEMBERIZER_API_TOKEN).
• Internet access to reach Rememberizer APIs.

Installation steps:

1. Install the MCP server package for Rememberizer locally using uvx:

uvx mcp-server-rememberizer

2. Set up environment variable with your Rememberizer API token. For example, in a local shell:

export REMEMBERIZER_API_TOKEN=your_rememberizer_api_token

3. Run the MCP server (the command above already starts the server in most setups). Ensure it is reachable by your Claude Desktop or MseeP AI Helper App integration; you may also configure a config file if your environment requires it.

4. If you are integrating with Claude Desktop or SkyDeck AI GenStudio, set up the mcpServers rememberizer entry to point to uvx with the token env var, as shown in the documentation snippet.

Prerequisites note: You may need to install uvx if not already installed and ensure your Python environment is compatible with the MCP server package. Consult Rememberizer documentation for any additional dependencies or version requirements.

Tips and considerations:

• Ensure REMEMBERIZER_API_TOKEN is kept secret and not committed to version control.
• The retrieve_semantically_similar_internal_knowledge tool expects a text input (match_this) and supports optional filters (from_datetime_ISO8601, to_datetime_ISO8601) and a configurable number of results (n_results).
• The smart_search_internal_knowledge tool can search across Slack, Gmail, Dropbox, Google Drive, and uploaded files; use a well-formed query and optional user_context to improve relevance.
• The list_personal_team_knowledge_documents tool supports pagination; use page and page_size to navigate large collections.
• If you encounter connectivity issues with Rememberizer, verify network access and API token validity, and check Rememberizer service status.
• In Claude Desktop configuration, include REMEMBERIZER_API_TOKEN in the environment for the rememberizer MCP server to function correctly.