skydeckai/mcp-server-rememberizer

开发工具

本页由 AI 翻译，技术细节请以原文为准。查看原文

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Repository Install

一个MCP服务器，用于实现对Rememberizer的全局访问

概览

一个用于与Rememberizer文档和知识管理API交互的模型上下文协议服务器。该服务器使大型语言模型能够通过Rememberizer搜索、检索和管理文档及集成。请注意，mcp-server-rememberizer 目前正在开发中，功能可能会有所更改。服务器提供两种资源访问：文档或 Slack 讨论 1. retrieve_semantically_similar_internal_knowledge - 发送一段文本，并从你连接的Rememberizer个人/团队内部知识和记忆库中检索余弦匹配 - 输入：- match_this（字符串）：最多400字句子，你希望找到语义相似的知识块 - n_results（整数，可选）：需返回语义相似的文本块数量。

概述（AI 翻译）

一个用于与Rememberizer文档和知识管理API交互的模型上下文协议服务器。该服务器使大型语言模型能够通过Rememberizer搜索、检索和管理文档及集成。

请注意，mcp-server-rememberizer 目前正在开发中，功能可能会有所更改。

组成部分

资源

服务器提供两种资源访问：文档或 Slack 讨论

工具

retrieve_semantically_similar_internal_knowledge

发送一段文本，并从你连接的Rememberizer个人/团队内部知识和记忆库中检索相似的余弦匹配
输入：
match_this（字符串）：最多400字的句子，你希望找到语义相似的知识片段
n_results（整数，可选）：返回语义相似文本块的数量。最多5块使用’n_results=3’，更多信息用’n_results=10’
from_datetime_ISO8601（字符串，可选）：ISO 8601格式的开始日期及时区（例如，2023-01-01T00：00：00Z）。使用此方法过滤特定日期的结果

本页由 AI 翻译，技术细节请以原文为准。

README

以下为原文 README 节选，完整内容请查看原文。

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

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
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

在 GitHub 查看完整 README

安装

uvx mcp-server-rememberizer

新手？先看 mcpskill.org 教程 →

配置

"mcpServers": {
  "rememberizer": {
      "command": "uvx",
      "args": ["mcp-server-rememberizer"],
      "env": {
        "REMEMBERIZER_API_TOKEN": "your_rememberizer_api_token"
      }
    },
}