Rememberizer AI MCP Server

Overview

The Rememberizer AI MCP server bridges large language models with Rememberizer’s rich document and knowledge‑management ecosystem. By exposing a set of semantic search, listing, and account‑information tools, it allows an AI assistant to pull context from a user’s personal or team knowledge base—including Slack threads, email archives, cloud documents, and custom uploads—directly into the model’s conversation. This solves a common pain point for developers: how to give an assistant instant, relevant access to disparate information sources without building custom integrations.

At its core, the server offers semantic retrieval through two primary tools: and . Both accept natural‑language queries up to 400 words, filter results by date ranges, and return the top n matches as plain text. These tools let developers embed powerful “memory‑aware” queries into prompts, enabling the assistant to surface prior conversations, policy documents, or project files on demand. The optional date filters make it possible to focus on recent updates or historical references, a feature particularly useful in compliance or audit scenarios.

Beyond search, the server provides resource discovery with and . These endpoints expose the underlying integrations (Slack, Gmail, Dropbox, Google Drive, uploads) and paginate large document collections. Combined with , developers can build self‑service dashboards or verify user permissions before querying data, ensuring secure and compliant access.

Typical use cases include:

• Contextual FAQ bots that pull policy text from company Slack or shared drives when a user asks a question.
• Research assistants that surface relevant academic papers and internal notes in one response, reducing the need for manual searching.
• Compliance monitors that retrieve recent policy updates and highlight changes across documents, feeding the assistant’s explanations to end users.
• Team knowledge bots that list new files added in a project folder, keeping everyone informed without leaving the chat interface.

Integration into an AI workflow is straightforward: a developer adds the Rememberizer MCP server as a tool provider in their prompt or agentic architecture. The assistant can then call the semantic search tools, parse the returned text, and weave it into its replies. Because MCP handles authentication, pagination, and error handling internally, developers can focus on crafting prompts that maximize the assistant’s usefulness rather than wrestling with API quirks.

What sets Rememberizer apart is its all‑in‑one knowledge ecosystem. Rather than pulling data from a single source, the server unifies multiple channels—Slack discussions, emails, cloud storage, and user uploads—into a single semantic search interface. This unified view eliminates the need for separate connectors, reduces latency, and ensures that every piece of relevant information is surfaced regardless of its original format. For developers building AI assistants that need to be “ever‑aware” of a user’s internal knowledge, the Rememberizer MCP server delivers a seamless, powerful, and secure bridge to that world.