Rememberizer MCP Server

The Skydeckai MCP Server Rememberizer bridges the gap between large language models and a powerful knowledge‑management platform. By exposing Rememberizer’s document search, listing, and account APIs through the Model Context Protocol, it lets AI assistants like Claude perform semantic searches, retrieve context from documents or Slack threads, and manage integrations—all without leaving the conversational interface. For developers building AI‑driven applications, this eliminates the need to write custom HTTP clients or handle authentication manually; instead, they can call concise, well‑defined MCP tools that encapsulate all the necessary logic.

At its core, the server offers two search primitives. performs a straightforward semantic similarity query over stored documents, returning the most relevant chunks as plain text. Its companion, , augments that query with an LLM agent layer, allowing the assistant to refine or expand the search based on conversational context. Both tools accept optional date filters and a result count, giving developers fine‑grained control over temporal relevance and payload size. The ability to search across documents or Slack discussions means a single tool can surface knowledge from disparate sources, making it ideal for internal knowledge bases or collaborative chat archives.

Beyond searching, the server provides utility tools that surface metadata and configuration. lists all data source integrations available to the account, while returns key account details. For document management, offers paginated listings of all stored documents, enabling developers to build discovery interfaces or audit logs. These tools together give a full picture of the knowledge ecosystem, allowing AI assistants to not only retrieve but also reason about where information resides.

In practice, the Rememberizer MCP server shines in scenarios that require contextual grounding. A support chatbot can pull the most recent policy documents when answering user queries, or a project management assistant can surface relevant Slack threads that discuss a feature implementation. By integrating seamlessly with Claude Desktop or any MCP‑compatible client, developers can compose complex workflows—search → summarise → act—using a minimal set of tool calls. The server’s design promotes modularity: each tool is stateless and can be composed with other MCP servers, making it a versatile component in larger AI pipelines.

Unique advantages of this implementation include its semantic search capability powered by Rememberizer’s embedding engine, the optional agentic augmentation that lets LLMs refine queries on the fly, and native support for Slack discussions. Together these features reduce friction in building knowledge‑aware assistants, ensuring that developers can focus on business logic rather than API plumbing.