Rememberizer MCP Server for Common Knowledge

Rememberizer MCP Server for Common Knowledge

Enables accessing and managing personal/team internal knowledge repository with tools for semantic search, smart search, document listing, and saving information for future recall.

Category
访问服务器

README

Rememberizer MCP Server for Common Knowledge

Common Knowledge: mcp的知识

Please note that rememberizer-mcp-mcp 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

Via SkyDeck AI Helper App

If you have SkyDeck AI Helper app installed, you can search for "Rememberizer" and install the rememberizer-mcp-mcp.

SkyDeck AI Helper

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

"mcpServers": {
  "rememberizer": {
      "command": "uvx",
      "args": ["rememberizer-mcp-mcp"]
    },
}

Usage with SkyDeck AI Helper App

With support from the Rememberizer MCP server for Common Knowledge, you can now ask the following questions in your Claude Desktop app or SkyDeck AI GenStudio

  • What is this Common Knowledge?

  • List all documents that it has there.

  • Give me a quick summary about "..."

  • and so on...

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选