メモ帳のドキュメントと知識管理 API とやり取りするためのモデルコンテキストプロトコルサーバー。 このサーバーでは、大規模なランゲージモデルを使用して、Rememberizerを使用して文書や統合を検索、取得、管理することができます。

開発中の[mcp-server-rememberizer]は現在、機能が変更される場合がありますのでご注意ください。

コンポーネント

リソース

サーバは、ドキュメントやSlackのディスカッションの2種類のリソースへのアクセスを提供します。

ツール

1. [retrieve_semantically_similar_internal_knowledge

• テキストのブロックを送信し、コサインの類似したマッチをコネクティッドリマイザ個人/チーム内部の知識とメモリリポジトリから取得
• 入力:
• [match_this(文字列):知識の著名なチャンクを見つけたい400語の文まで
• [n_results] (整数、オプション): テキストの部分的に類似したチャンクの数を返す。 ‘n results=3’ を最大 5 に使用し、 ‘n results=10’ はより詳細な情報
• [from_datetime_ISO8601(文字列、オプション):タイムゾーンのISO 8601フォーマットで開始日(例、2023-01-01T00:00:00Z)。 特定の日付から結果をフィルタリングするためにこれを使用します

MCP Server Rememberizer

A 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