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2025-04-01-rising

  • 精選方式: RISING

討論重點​

以下是25篇討論重點的條列式總結，包含核心主題與詳細內容，並附上對應的文章錨點連結：

1. v0.7.3 Update: Dive, An Open Source MCP Agent Desktop​

  • 核心主題：Dive桌面應用程式的功能更新與特色介紹
    • 跨平台支援：兼容Windows/Linux，整合多種LLM工具調用功能。
    • 0.7.3版本更新：
      • 多模型切換（OpenAI/Claude/Gemini等）與自訂API配置。
      • 使用者體驗優化（可編輯訊息、自動更新、介面改進）。
    • 推廣：提供GitHub下載連結，鼓勵開發者試用。

2. Hype-less opinion of MCP​

  • 核心主題：批判AI技術（如MCP）的過度炒作
    • 呼籲以專業角度（如程式設計師觀點）評估技術，避免盲從非專業人士的狂熱宣傳。

3. Google is looking into MCP! can we get Sundar do AMA in /r/mcp?​

  • 核心主題：要求Google改進Workspace的MCP伺服器
    • 批評Google Maps MCP的REST API效能問題，呼籲用戶在Issue Tracker反饋需求。

4. Open WebUI new release; OpenAPI support and MCP bridge​

  • 核心主題：OpenWebUI v0.6.0強化MCP整合
    • 支援混合架構（Ollama/vLLM + MCP伺服器），平衡穩定性與前沿技術。

5. Jupyter Notebook MCP: work as a professional data analyst​

  • 核心主題：Claude AI與Jupyter Notebook的MCP整合
    • 透過WebSocket實現自然語言操控筆記本（執行代碼、生成分析報告）。

6. Is MCP inspector enough?​

  • 核心主題：MCP Inspector工具的局限性與改進提案
    • 提議開發類Postman的桌面應用，改善多伺服器管理效率。

7. Brave search API T&C violation???​

  • 核心主題：Brave Search MCP實作可能違反API條款
    • 爭議點：開源專案使用Claude整合Brave API是否合規。

8. Figma MCP tutorial​

  • 核心主題：未提供具體內容（需參考影片標題/描述）。

9. Help me understand MCP in a multi-tenant cloud application​

  • 核心主題：多租戶雲端應用中MCP的角色爭議
    • 討論混合架構（前端直接調用工具 vs. 雲端協調）是否為反模式。

10. How to start MCP?​

  • 核心主題：MCP認證技術與跨系統整合的學習路徑
    • 涵蓋API開發、微軟生態工具（如Azure）的實務應用。

（因篇幅限制，以下為簡要條目，格式同上）
11. Rich Context AI

  • 知識管理系統革新：AI自動整合碎片化資訊，保留決策脈絡。
  1. MCP that returns the docs
  • 自動化文件搜尋工具：從網域或預設庫提取llms.txt內容。
  1. MCP for Shopify Api
  • Shopify多通路管理：整合訂單/庫存數據，可能結合Claude AI。
  1. Why sometimes work and sometimes don't
  • **工具載入不

文章核心重點​

以下是根據每篇文章標題和內容生成的一句話摘要（條列式輸出）：

  1. v0.7.3 Update: Dive, An Open Source MCP Agent Desktop
    Dive 推出 0.7.3 版本，強化多模型支援與操作體驗，提供跨平台 LLM 開發工具整合。

  2. Hype-less opinion of MCP
    作者呼籲理性看待 MCP 技術，避免受非專業炒作影響。

  3. Google is looking into MCP! can we get Sundar do AMA in /r/mcp?
    用戶要求 Google 改進 Workspace 的 MCP 伺服器效能，並號召社群參與連署。

  4. Open WebUI new release; OpenAPI support and MCP bridge
    OpenWebUI v0.6.0 新增 MCP 伺服器整合，平衡穩定工具與實驗性功能。

  5. Jupyter Notebook MCP: work as a professional data analyst
    JupyterMCP 透過 Claude AI 操控筆記本，實現自然語言分析與自動化代碼執行。

  6. Is MCP inspector enough?
    探討 MCP Inspector 工具在多伺服器管理上的不足，建議開發更直覺的替代方案。

  7. Brave search API T&C violation???
    討論 Brave Search 的 MCP 實作是否違反 API 使用條款，涉及開源合法性爭議。

  8. Figma MCP tutorial
    （無法存取內容，建議查看影片標題與描述判斷主題）

  9. Help me understand MCP in a multi-tenant cloud application
    分析 MCP 在多租戶雲端應用中的角色，探討混合架構潛在問題。

  10. How to start MCP?
    提供 MCP 認證技術與跨系統整合的學習路徑與實作方法。

  11. Introducing Rich Context AI: Knowledge Management Reimagined for the AI Era
    Rich Context AI 透過脈絡化整合碎片資訊，解決現代知識管理痛點。

  12. MCP that returns the docs
    開源工具 MCP Agent 自動搜尋並返回指定網域的 llms.txt 文件，簡化開發流程。

  13. MCP for Shopify Api
    GitHub 專案開發 Shopify 多通路管理工具，可能結合 Claude AI 優化流程。

  14. Why sometimes work and sometimes don't
    反映 Claude Desktop 工具載入不穩定問題，推測可能與配置或環境變數有關。

  15. MCP server of whatsapp using nodejs
    TypeScript 實作 WhatsApp 多裝置協議，支援第三方客戶端開發與自動化。

  16. Using MCP for RAG workflow
    探討如何用 MCP 伺服器自動化文檔爬取、向量儲存與語義搜索，優化 RAG 流程。

  17. Revit MCP – Allows AI to interact with Autodesk Revit via the MCP protocol
    （推測）透過 MCP 協議讓 AI 操控 Revit，實現建築專案數據自動化管理。

  18. Advantages of MCP
    分析 MCP 在工具整合、標準化參數與 AI 自動化探索上的優勢。

  19. I created a tool to create MCPs
    推出 AI 驅動工具，簡化 MCP 伺服器開發的初始設定與文件生成。

  20. Puppeteer (Browser control) MCP tutorial
    （無法存取內容，建議查看影片標題與描述判斷主題）

  21. MCP server library for D language
    （無法存取內容，推測為 D 語言開發的 MCP 伺服器函式庫）

  22. Marketplace for Claude
    討論 Claude 的隱私保護機制與 MCP 伺服器連線失敗的技術問題。

  23. PagerDuty MCP Server
    （推測）整合 PagerDuty 與 MCP 伺服器，強化事件管理與自動化警報。

  24. New to MCP—Tips & Things I Should Know Before Diving In?
    徵求 MCP 設置建議、資源與常見陷阱，加速新手入門。

  25. MCP Outline Server
    （推測）透過 MCP 協議讓 AI 管理 Outline 文件服務，支援全文搜索與協作。

目錄​

1. v0.7.3 Update: Dive, An Open Source MCP Agent Desktop​

這篇文章的核心討論主題是 Dive 桌面應用程式的功能更新與特色介紹，具體聚焦於以下重點：

  1. Dive 的定位與核心價值

    • 跨平台（Windows/Linux）工具，支援所有能進行工具調用（tool calls）的 LLM。
    • 簡化 MCP Server 安裝流程，提供高效的開發工具整合與即時嵌入功能。

  2. 0.7.3 版本更新內容

    • 多模型支援與切換：
      • 兼容多種主流 LLM 服務（如 OpenAI、Claude、Gemini 等），並允許自訂模型。
      • 支援同一供應商的多組 API 金鑰或配置切換。
    • 使用者體驗與效能優化：
      • 新增可編輯訊息、重新生成 AI 回應、自動更新等功能。
      • 介面改進（如折疊工具區塊、快捷鍵邏輯調整）。
      • API 金鑰輸入錯誤提示優化、MCP 範例程式碼精簡等。
    • 背景運作與自動啟動：支援最小化至背景運行與開機自啟。

  3. 推廣與體驗

    • 提供 GitHub 發布連結，鼓勵使用者下載試用新版本。

總結：文章主要宣傳 Dive 作為一款高效 LLM 開發工具的最新功能升級，強調其靈活性（多模型支援）與易用性（操作優化），目標受眾為開發者。

內容​

Dive is a desktop application for Windows and Linux that supports all LLMs capable of making tool calls. It is currently the easiest way to install MCP Server. Offering real-time tool call embedding and high-performance system integration, Dive is designed to provide developers with more flexible and efficient development tools.

0.6.0 → 0.7.3 Update Summary

  1. Multi-Model Support & Switching

  • Supported models: OpenAI GPT-4, ChatGPT API, Azure OpenAI, Claude, AI21, Gemini, HuggingChat, Mistral AI, deepseek, AWS, and other LLM services. Custom models are also supported.

  • Multi-model Switching: Switch between multiple MCP Servers. You can use multiple sets of keys or different configurations for the same LLM provider, and easily switch between them.

  1. User Experience & Performance Optimization

  • Editable Messages: Modify messages that have already been sent.

  • Regenerate Responses: Supports regenerating AI responses.

  • Auto Updates: Now supports automatic updates to the latest version.

  • Interface and Operation Enhancements: Collapsible tool_calls and tool_result sections; pressing ESC while the sidebar is open will prioritize closing the sidebar instead of interrupting AI responses.

  • API Key Configuration Improvements: Displays error messages in red for incorrect inputs, and error messages disappear automatically when switching providers.

  • MCP Server Default Example Optimizations: The echo example has been updated from CJS format to ESM, reducing file size.

  • Background Operation and Auto-Start: The app can be minimized to the background and supports auto-start on boot.

Try it out! 👇🏻

https://github.com/OpenAgentPlatform/Dive/releases

討論​

評論 1:

Dive is a desktop application for Windows and Linux that supports all LLMs capable of making tool calls. It is currently the easiest way to install MCP Server. Offering real-time tool call embedding and high-performance system integration, Dive is designed to provide developers with more flexible and efficient development tools.

0.6.0 → 0.7.3 Update Summary

  1. Multi-Model Support & Switching
  • Supported models: OpenAI GPT-4, ChatGPT API, Azure OpenAI, Claude, AI21, Gemini, HuggingChat, Mistral AI, deepseek, AWS, and other LLM services. Custom models are also supported.
  • Multi-model Switching: Switch between multiple MCP Servers. You can use multiple sets of keys or different configurations for the same LLM provider, and easily switch between them.
  1. User Experience & Performance Optimization
  • Editable Messages: Modify messages that have already been sent.
  • Regenerate Responses: Supports regenerating AI responses.
  • Auto Updates: Now supports automatic updates to the latest version.
  • Interface and Operation Enhancements: Collapsible tool_calls and tool_result sections; pressing ESC while the sidebar is open will prioritize closing the sidebar instead of interrupting AI responses.
  • API Key Configuration Improvements: Displays error messages in red for incorrect inputs, and error messages disappear automatically when switching providers.
  • MCP Server Default Example Optimizations: The echo example has been updated from CJS format to ESM, reducing file size.
  • Background Operation and Auto-Start: The app can be minimized to the background and supports auto-start on boot.

Try it out! 👇🏻

https://github.com/OpenAgentPlatform/Dive/releases

2. Hype-less opinion of MCP​

這篇文章的核心討論主題是對當前人工智慧（AI）模型互動方式（如MCP）的過度炒作（hype）提出質疑，並呼籲以更理性、專業的角度（如程式設計師或電腦科學家的觀點）來評估這些技術，而非盲目跟隨非專業人士（如「腳本小子」或跟風程式設計師）的狂熱情緒。作者強調應避免被AI領域的過度行銷（如「AI bros」的宣傳）所誤導，回歸技術本質的討論。

內容​

I know many of you are hyped by MCP, but I want an actual programmer/computer scientist hype-less opinion on this thing, not just script kiddies/vibe coders. Because there's always a new way to interact with AI models that are hyped by AI bros

討論​

評論 1:

Engineer with 10+ YoE; I'll answer with a comparison to a different protocol: Language Server Protocol (LSP).

The Language Server Protocol was released in 2016; before that, development IDEs used to need to implement specific tools for each and every programming language it wanted to support. This meant that a developer's favorite programming language (Javascript, Python, Java, or perhaps a more niche language) may work very poorly in VSCode, but works amazing in Sublime Text - and if you wanted to use a more niche editor (e.g. VIM in the terminal), then at best you would be stuck with some janky open-source plugin for your language, and likely wouldn't have any tool support.

Then came LSP: Microsoft standardized a protocol of Editor features that Programming Languages could support (e.g. syntax highlighting, auto-complete, lint errors, etc). This meant very important improvements for both the LSP Clients (IDEs) and the LSP Servers (Programming Language tools):

- Programming Languages (the LSP servers): PLs and their ecosystems no longer needed to implement features against a given editor; so instead of Typescript needing a "Typescript-Sublime-Server" and "Typescript-VSCode-Server", there's just a "Typescript-LSP-Server".
- IDEs (the LSP clients) no longer needed to "support" a given programming language; as long as the IDE implemented an LSP client, any LSP server could connect to it. So VIM doesn't care what language you're using, its features work the same across any LSP-supported language

No LSP vs LSP diagram: https://code.visualstudio.com/assets/api/language-extensions/language-server-extension-guide/lsp-languages-editors.png

Fast forward to today, LSP has absolutely transformed the Development tooling world: it allows even the most-niche Programming Languages to have INCREDIBLE developer experiences in your favorite IDE, since they don't need to directly partner with / depend on the IDE clients like Cursor or VSCode, and can instead simply focus on creating an LSP server.

On the client-side of things, end-users (developers) no longer need to pick an IDE based on whether or not the IDE supports their favorite language, and they can instead pick an IDE based on its actual editor capabilities (e.g. using Cursor for its latest AI capabilities).

Of course, we're still in the middle of the LSP transformation, and some languages/companies have a vested interest in not using a standardized protocol (e.g. JetBrains IDEs, Apple's XCode, etc) - however, LSP is increasingly becoming the defacto way to connect an editor to a language's tooling.

-- MCP in 2025 --

MCP is very similar to LSP; in fact I wouldn't be surprised if it's APIs were the main source of inspiration for MCP.

In the above comparison, if you replace "Programming Languages" with 3rd Party APIs (e.g. Stripe, Supabase, etc), and you replace "IDEs" with "AI Chat Clients", then you get MCP .

MCP, if successful, would remove the need for a company like Supabase to support integrations with Cursor, Claude, OpenAI, LangChain, Vercel AI Sdk, etc - instead, they simply have a Supabase MCP Server, which can be used by ANY of those MCP clients in the future. This will allow even the smallest startups to enable robust AI solutions for their users to use in any AI client, with minimal setup; furthermore, MCP will eliminate the need for any MCP Client to create their own marketplace of integrations (might be the end for the OpenAI GPT Marketplace?), which means end users will have a much easier time using their tools on any AI platform.

IMO, MCP seems like it's in the right hands with Anthropic - I believe it has a high chance of becoming the defacto solution for integrating third party tools and APIs with LLMs.

EDIT: Some good info on how LSP solved the same problem:
- https://code.visualstudio.com/api/language-extensions/language-server-extension-guide#why-language-server

評論 2:

I developed with MCP since November, before thanksgiving, months before anyone knew what the hell MCP was.

MCP is like the internet, or AI generally. It’s incredible if used correctly, and there are things you can do that would otherwise take hours on automation and connection setup.

It can also be useless garbage. Read the docs. Understand what’s happening under the hood. Understand the fundamental requirements and internals of a good server/client.

If you do that you’re doing more than 99% of users, and depending on your use case, will either have a good time, great time, or game changing experience time. If you don’t do that, who knows…

評論 3:

I recently built an mcp server meant to be deployed in production. It was my second such one - my first was built using the spec and FastAPI, and the second was using the Python SDK / FastMCP. Here's my thoughts: There is absolutely nothing that you can do with MCP the you can't do with more traditional methods in programming. But that's not the point... The point is to do the thing in the same way all the time - enabling interoperability.

評論 4:

Someone answered in another thread. It's a protocol.

評論 5:

It’s a protocol. If you spend any time as a career engineer you’ll encounter many of these.

It’s just a specification. Nothing more. Nothing less.

It’s a standardized way for tool definitions and communication.

It doesn’t allow you to do anything you couldn’t before.

It just STANDARDIZES the way we do it so we can more easily share and implement tooling.

3. Google is looking into MCP! can we get Sundar do AMA in /r/mcp?​

The core discussion topic of the article is a request for Google to improve its Managed Client Platform (MCP) server for Workspace apps (e.g., Gmail, Drive, Calendar, Meet) by addressing issues with the current implementation, particularly criticizing the poor performance of the Google Maps MCP behind REST APIs. The author urges users to support this request by starring and commenting on the linked Google Issue Tracker thread (https://issuetracker.google.com/401270828).

Key points:

  1. Criticism of Google Maps MCP: The current MCP implementation is described as subpar ("busted").
  2. Call for Better MCP Integration: Any future MCP improvements for Workspace should avoid the flaws seen in Google Maps' REST API integration.
  3. Community Engagement: Encourages users to signal demand for fixes via the issue tracker (starring/commenting).

Theme: Advocacy for a more reliable and efficient MCP infrastructure for Google Workspace services.

內容​

For a Google provided MCP server for Workspace (Gmail, Drive, Sheets, Calendar, Sheets, Meet, etc), star ⭐ and comment on this issue: https://issuetracker.google.com/401270828! The google maps MCP is behind their REST APIs and busted .. if they go in on MCP it has to be better than that

討論​

評論 1:

For a Google provided MCP server for Workspace (Gmail, Drive, Sheets, Calendar, Sheets, Meet, etc), star ⭐ and comment on this issue: https://issuetracker.google.com/401270828!

評論 2:

The google maps MCP is behind their REST APIs and busted .. if they go in on MCP it has to be better than that

4. Open WebUI new release; OpenAPI support and MCP bridge​

根據提供的 GitHub 連結（OpenWebUI 的 v0.6.0 版本發布頁面）以及用戶的補充描述，核心討論主題可總結為：

  1. 工具整合的強化
    該版本重點在於擴展對多種成熟伺服器/API 的支援（如 Ollama、vLLM 等），同時新增對新興 MCP（Model Control Plane）伺服器的整合能力，形成一個「混合型」（hybrid）架構。這讓使用者能更靈活地結合現有工具與新興技術。

  2. 對 MCP 生態的支援
    特別強調對新興 MCP 伺服器的整合，可能是為了因應模型部署與管理的標準化趨勢（類似 Kubernetes 對容器編排的角色），降低複雜環境中的操作門檻。

  3. 成熟度與擴展性平衡
    透過同時支援穩定工具（如長期維護的 API）和實驗性功能（如 MCP），專案試圖在穩定性和前沿技術之間取得平衡，吸引更廣泛的開發者與企業用戶。

  4. OpenWebUI 的定位演進
    此更新反映專案從單純的 Web 介面工具，轉向成為「整合中樞」，協助用戶統一管理分散的 AI 模型服務。

若需更精確的分析，建議直接查閱發布頁面的詳細更新日誌（如新增功能列表或破壞性變更說明），但根據現有資訊，上述主題已涵蓋核心方向。

內容​

https://github.com/open-webui/open-webui/releases/tag/v0.6.0

They've supported tools for a while, but this feels like a nice hybrid to add a large amount of mature servers/APIs and integration for emerging MCP servers.

討論​

無討論內容

5. Jupyter Notebook MCP: work as a professional data analyst​

這篇文章的核心討論主題是 Jupyter Notebook MCP (JupyterMCP) 的整合功能與應用，重點在於透過 Model Context Protocol (MCP) 將 Claude AI 與 Jupyter Notebook 連接，實現人工智慧直接控制與互動的筆記本操作。具體涵蓋以下方向：

  1. 技術整合

    • 透過 WebSocket 伺服器建立雙向通訊，讓 Claude AI 能直接操控 Jupyter Notebook（v6.x 以上版本）。

  2. 核心功能

    • 筆記本操作：以自然語言指令管理儲存格（插入、編輯、執行）、創建/保存筆記本。
    • 多語言支援：執行 Python、Stata 等 Jupyter 支援的語言代碼。
    • 輸出解析：Claude 能讀取文字、圖像等執行結果，並提供分析與解釋（如統計報表、視覺化圖表）。

  3. 實際應用案例

    • 教學演示：自動生成 Seaborn 庫的教學簡報（含 Markdown 說明、代碼執行與投影片設定）。
    • 統計分析：解決 Stata 統計問題集，直接執行分析並解讀結果（如信賴區間計算）。

  4. 實驗性質與風險提示

    • 強調工具仍屬實驗階段，需謹慎處理自動生成的代碼執行。

總結：文章聚焦於 AI 與 Jupyter Notebook 的深度協作，展示如何透過 Claude 提升筆記本的互動效率與分析能力，同時提供開源專案的實例與資源連結。

內容​

Jupyter Notebook MCP (JupyterMCP) connects Jupyter Notebook to Claude AI through the Model Context Protocol (MCP), enabling Claude to directly interact with and control Jupyter notebooks. This integration allows prompt-assisted notebook creation, cell management, code execution, result interpretation, and more.

Features:

  • Two-way communication: Connect Claude AI to Jupyter Notebook (v6.x) via a WebSocket-based server.

  • Cell manipulation: Insert, edit, execute, and manage notebook cells through natural language prompts.

  • Notebook management: Create, manage, and save notebooks efficiently.

  • Output retrieval: Get text outputs, images, and analysis interpretations directly from Claude.

  • Multilanguage support: Execute code in Python, Stata, and potentially other languages supported by Jupyter kernels.

  • Result interpretation: Leverage Claude’s powerful reasoning capabilities to analyze and interpret statistical results, visualizations, and more.

Jupyter Notebook MCP making a presentation

In this demo, Claude was asked to:

  • Create a notebook presentation about Python’s Seaborn library.

  • Insert markdown and code cells describing key concepts clearly and concisely.

  • Execute Python code demonstrating common Seaborn plots.

  • Set appropriate slide types for each cell to create an engaging notebook-based presentation.

In another demo, Claude:

  • Solved a real statistics problem set using Stata.

  • Ran statistical analyses directly from the notebook.

  • Interpreted the statistical results (e.g., calculating and analyzing 95% confidence intervals).

Jupyter Notebook MCP solving statistics problem set with Stata

Full details at repo: https://github.com/jjsantos01/jupyter-notebook-mcp

⚠️ Disclaimer: Experimental tool—use cautiously, especially when executing arbitrary code.

討論​

評論 1:

Was looking for thsi just 5 min before lol, thanks for the share

6. Is MCP inspector enough?​

該文章的核心討論主題是：
「探討現有 MCP Inspector 工具在調試 MCP 伺服器時的局限性（如無法高效管理多個伺服器），並提出開發一個類似 Postman 的桌面應用程式以改善用戶體驗與功能豐富性」。

重點摘要：

  1. 問題描述：新手使用 MCP Inspector 時遇到不便，尤其是多伺服器管理效率低下。
  2. 解決方案提案：建議開發直觀、功能更完善的桌面應用（類 Postman 設計），優化 MCP 伺服器的互動與管理體驗。
  3. 核心議題：工具的使用痛點與潛在改進方向，強調用戶友好性與功能性提升的需求。

內容​

I'm new to MCP and trying to debug with the MCP server. When using MCP Inspector, I found several inconveniences, such as the inability to manage multiple MCP servers efficiently. I'm wondering whether it would be better to develop a desktop application, similar to Postman, that provides a more user-friendly and feature-rich experience for managing and interacting with MCP servers.

討論​

無討論內容

7. Brave search API T&C violation???​

根據提供的圖片連結（標題為「Brave Search MCP Server Implementation with Claude」）和相關討論，核心討論主題聚焦於：

「Brave Search 的 MCP（Model Context Protocol）伺服器實作是否違反 Brave Search API 的使用條款？」

具體爭議點可能包括：

  1. API 條款合規性：使用 Claude 作為客戶端與 Brave Search API 互動，是否符合 Brave 官方對 API 訂閱的使用限制（例如禁止自動化抓取或第三方整合）。
  2. 開源實作的合法性：GitHub 上的開源專案（連結）是否涉及未授權的 API 封裝或商業用途，違反 Brave 的服務條款。
  3. 技術與倫理爭議：此類實作對 Brave 的商業模式（如付費 API 訂閱）可能造成的影響，以及開源社群與企業政策之間的衝突。

由於無法直接查看圖片完整內容，以上總結基於標題、GitHub 專案名稱及常見的 API 條款爭議情境推論。若需更精確分析，建議提供更多上下文或條款具體內容。

內容​

連結： [https://preview.redd.it/gdguxtqdk5se1.png?width=859&format=png&auto=webp&s=70d92dad4cb2ff388e1945cc8590a69c9e218d49

Isn't Brave Search MCP Server implementation with Claude as client violates the Brave search API subscription terms of use?](https://preview.redd.it/gdguxtqdk5se1.png?width=859&format=png&auto=webp&s=70d92dad4cb2ff388e1945cc8590a69c9e218d49

Isn't Brave Search MCP Server implementation with Claude as client violates the Brave search API subscription terms of use?)

討論​

無討論內容

8. Figma MCP tutorial​

由於我無法直接訪問 YouTube 影片內容，因此無法總結該影片的核心討論主題。不過，您可以根據以下步驟自行分析影片內容：

  1. 影片標題與描述：查看影片的標題和說明欄，通常會直接反映核心主題。
  2. 開頭與結尾：影片的前幾分鐘和結尾部分通常會點明主要討論的重點。
  3. 重複出現的關鍵詞：注意影片中反覆提到的術語或概念。
  4. 評論區線索：觀眾的討論可能圍繞影片的核心議題展開。

如果您能提供影片的標題、描述或關鍵內容摘錄，我可以幫助您進一步分析總結！

內容​

連結： https://youtu.be/3nYDUqlA13s?si=YA3PyCl75aMTHmb2

討論​

無討論內容

9. Help me understand MCP in a multi-tenant cloud application​

核心討論主題總結：​

  1. MCP（Model Control Plane）在雲端多租戶應用中的角色定位：
    文章主要探討如何將MCP架構整合到「前端（FE）簡化、AI邏輯集中於雲端」的多租戶應用中，尤其是當MCP伺服器與工具部署在本地網路（而非雲端伺服器）時，釐清「誰是MCP客戶端」（雲端應用或前端）的問題。

  2. 混合架構下的MCP工具存取模式：
    討論兩種情境：

    • 情境一：所有工具僅透過MCP伺服器提供，雲端應用作為MCP客戶端，協調前端請求與MCP工具調用。
    • 情境二：部分工具需由前端直接調用（如經用戶授權），此時前端成為MCP客戶端，並需與雲端應用協作（透過HTTP）完成對話流程。
      此模式可能形成「混合MCP+HTTP」架構，引發是否為「反模式」的疑慮。

  3. 企業場景的特殊需求與妥協：
    作者強調企業環境中可能存在獨特的業務與安全需求（如工具需隔離於本地網路、前端需直接參與工具調用等），導致架構設計偏離典型MCP模式，需在技術合理性與現實限制間取得平衡。

關鍵問題提煉：​

  • 技術角色定義：雲端應用與前端在MCP架構中的客戶端角色如何劃分？
  • 架構合理性：混合MCP與HTTP通訊是否違背MCP設計原則？是否存在更優解？
  • 企業約束的影響：特殊需求（如安全合規、工具分佈式部署）如何主導技術選型？

隱含討論方向：​

內容​

A lot of the early information for MCP is about running MCP servers and clients locally, or at the very least running the AI application locally, where only the LLM is hosted remotely. I'm having trouble understanding how MCP fits into multi-tenant cloud apps, where the frontend (FE) is dumb, and most of the AI app is in the cloud.

My AI app has a basic FE that POSTs conversation-like objects to a cloud-hosted AI app where user-configured prompts, tool output, and server-side conversation data are combined and sent to the LLM. The result is streamed back to the FE and rendered.

  1. Supposing I wanted to put all tools behind MCP servers, and these MCP servers are on the network and not in cloud-app servers. Then is the cloud app the MCP client? Or would/can my FE be the MCP client?

  2. What if, further to that, I wanted to access some MCP server tools only from the FE. In this case the cloud app would need to know about the MCP servers, respond to the FE POST by asking for an MCP tool call, the FE would execute that (possibly with user approval) and then POST the requested info back to the cloud app to be processed in the current conversation. In this case would my FE be the MCP client? Does the setup seem like an anti-pattern, where tools are actually accessible to my cloud app through some kind of hybrid MCP + HTTP?

Some of you will read this and think, why would you do that, and the answer might be because I didn't know any better, but it also might be because enterprise software can have some pretty weird business and security requirements. Answer if you can, and ask questions if you think some details are missing in my question.

討論​

評論 1:

FE -> MCP Client (cloud app) -> MCP Server
Having the whole client on the frontend can be an issue cause where do you put the api keys. You might be able to split part of the client between the FE and the cloud app. But a lot depends on your setup, e.g. are the servers stateless?

10. How to start MCP?​

該文章的核心討論主題是：如何學習並實現Microsoft Certified Professional (MCP) 認證技術與不同應用程式的整合連接。具體可能包含以下方向：

  1. MCP相關技術的學習重點：

    • 掌握微軟認證（如Azure、SQL Server、Power Platform等）的核心技術，以確保基礎能力。

  2. 跨應用程式整合的關鍵技能：

    • 學習API開發、數據交換格式（如JSON/XML）、中介軟體（如Azure Logic Apps）或協定（如REST、OAuth）等整合技術。

  3. 實際應用場景與工具：

    • 如何透過微軟生態（如Power Automate、Azure服務）或第三方工具實現系統間連接，並可能探討案例或最佳實踐。

  4. 認證與實務的結合：

    • 將MCP認證的理論知識應用於實際整合需求，例如企業系統（ERP/CRM）或雲端服務的串接。

總結：文章聚焦於「技術學習路徑」與「實作方法」，以達成MCP認證技術與多元應用系統的協作與整合。

內容​

What to learn to make MCP connecting with different applications

討論​

評論 1:

You can vibe an MCP server here: mcp.getflow.dev if you are a beginner.

評論 2:

If you want to RTDM

https://modelcontextprotocol.io/quickstart/server

評論 3:

Easiest way would be visual studio code. Roo code or cline extension hand built in mcp installing and building capabilities. Gemini pro api key

11. Introducing Rich Context AI: Knowledge Management Reimagined for the AI Era​

這篇文章的核心討論主題是：
「如何透過『Rich Context AI』解決現代知識工作者面臨的資訊碎片化與缺乏脈絡的問題，並重新設計知識管理系統以提升團隊效率」。

具體重點如下：

  1. 問題背景：

    • 知識工作者面臨資訊分散於多平台（如Slack、郵件、文件），導致搜尋困難、決策缺乏完整脈絡。
    • 傳統知識管理系統因需手動輸入、孤立於工作流程外而失效。

  2. 解決方案：

    • Rich Context AI 的核心是「連結資訊與其脈絡」，透過自動化整合工具中的知識，保留決策背景、關聯性與來源，使資訊更易於查找與應用。

  3. 核心價值：

    • 強調「脈絡」（Context）的重要性，例如資訊的來源、討論過程、相關決策等，能將原始數據轉化為可行動的知識。

  4. 適用對象：

    • 從新創到大型企業均面臨知識孤島、資訊流失等挑戰，此系統旨在滿足不同規模團隊的需求。

  5. 目標：

    • 在資訊過載的環境中，確保團隊能快速獲取「帶有脈絡的知識」，減少重複勞動並提升決策品質。

總結：文章推廣一種新型知識管理工具，主張透過「脈絡化」整合碎片資訊，解決現代工作中的知識管理痛點。

內容​

In today’s digital landscape, knowledge workers face a common challenge: valuable information is scattered across multiple platforms, making it difficult to find what we need when we need it. We spend significant time searching through Slack conversations, emails, documents, and meeting notes to piece together the context we need.

The Knowledge Management Challenge

Traditional knowledge management systems often fail to address this problem effectively. They typically require manual input, exist in isolation from our workflows, and struggle to connect related information meaningfully. The result is lost context, duplicated effort, and decisions made without the complete picture.

This is why I created Rich Context AI — a fresh approach to knowledge management designed for today’s information-rich work environments.

What is Rich Context AI?

Rich Context AI aims to transform how teams capture, organize, and access their collective knowledge. The vision is to create a knowledge management solution that works with your existing tools and workflows, making information discovery more intuitive and efficient.

The core concept behind Rich Context is simple but powerful: connecting information to its context makes it more valuable and actionable.

Why Context Matters

The name “Rich Context” reflects the fundamental philosophy of the project. Information without context is just data. When we understand where information comes from, who created it, what decisions it influenced, and how it relates to other knowledge, we can use it more effectively.

When team members need to understand past decisions or locate specific information, having access to the complete picture — including the discussions and reasoning that led to the current state — is invaluable.

For Teams of All Sizes

Knowledge management challenges affect organizations of all sizes:

  • Startups: Need to preserve critical knowledge as they grow

  • Mid-size companies: Face increasing challenges with knowledge silos between teams

  • Enterprises: Must ensure knowledge isn’t lost during transitions and is accessible across the organization

Learn More

I’m excited to introduce Rich Context AI and start this journey. To learn more about the project and stay updated on our progress:

Visit our website

In a world where information overload is the norm, Rich Context AI aims to help teams work smarter by ensuring the right knowledge is always accessible — complete with the context that makes it truly valuable.

Rich Context AI is a new knowledge management solution focused on solving the information overload problem. To learn more, visit richcontext.ai.

討論​

評論 1:

You lose me at "in today's digital landscape"

評論 2:

Another Docs as a MCP or Notion AI?

評論 3:

Tune your positioning statement, this story is not addressing the problem your user is trying to solve.

12. MCP that returns the docs​

這篇文章的核心討論主題是作者成功開發了一個名為「MCP Agent」的測試版工具，其主要功能是自動搜尋並返回指定網域中的 llms.txt 文件內容。重點包括：

  1. 功能描述：

    • 自動搜尋目標網域的 llms.txt 文件。
    • 若本地找不到文件，則回退到預設的 llmtxt.dev 中心庫。
    • 直接返回文件內容，無需手動記憶或輸入文件路徑。

  2. 應用場景：

    • 支援在代碼編輯器（如 Cursor）中使用，簡化開發流程。

  3. 開源性質：

    • 工具為開源項目，暗示可公開存取或貢獻。

  4. 用戶體驗：

    • 強調「無需記憶路徑」的便利性，並以幽默語氣（如「teasing us or whaat? OH MY GOSH」）表達興奮之情，反映此工具解決了實際痛點（「MUCH WANTED」）。

總結：文章主要介紹一個能自動化獲取 llms.txt 文件的工具，突出其便捷性、開源特性及對開發效率的提升。

內容​

I finally created the MCP I wanted—a beta version!

No need to add the llms.txt file in docs anymore; just ask this MCP Agent for it.

What does it do?

- Searches for llms.txt files on the specified domain

- Falls back to the llmtxt\.dev hub if no local file is found

- Returns the documentation content

Most importantly, it works in cursor and probably (I haven't tested yet) in other code editors. Awesome! Link? Open source? Yes, it returns the llms.txt file, but you don’t need to remember the path for each domain, you let llm to ask for it teasing us or whaat? OH MY GOSH

MUCH WANTED

討論​

評論 1:

I finally created the MCP I wanted—a beta version!

No need to add the llms.txt file in docs anymore; just ask this MCP Agent for it.

What does it do?
- Searches for llms.txt files on the specified domain
- Falls back to the llmtxt\.dev hub if no local file is found
- Returns the documentation content

Most importantly, it works in cursor and probably (I haven't tested yet) in other code editors.

評論 2:

Awesome! Link? Open source?

評論 3:

Yes, it returns the llms.txt file, but you don’t need to remember the path for each domain, you let llm to ask for it

評論 4:

teasing us or whaat?

評論 5:

OH MY GOSH

MUCH WANTED

13. MCP for Shopify Api​

根據提供的 GitHub 連結（shopify-mcp），該專案的核心討論主題是 Shopify 商店的「多通路管理」（Multi-Channel Management）工具開發，可能涉及以下關鍵內容：

  1. 功能目標

    • 幫助 Shopify 商家整合與管理多個銷售通路（如線上商店、第三方平台、實體店面等），集中處理訂單、庫存和商品數據。

  2. 技術實現

    • 使用 Shopify API 或其他技術框架開發工具，可能包含自動化流程或數據同步功能。

  3. 與 Anthropic Claude 的關聯

    • 專案可能結合 Claude 的 AI 能力（如自然語言處理）來優化多通路管理，例如自動化客服或數據分析（需進一步確認代碼或文檔）。

  4. 開源協作

    • 通過 GitHub 公開代碼，鼓勵開發者參與改進或自定義功能。

若需更精確的總結，建議直接查看專案的 README 或代碼內容，確認具體技術細節和應用場景。當前資訊僅基於專案名稱和常見的 Shopify 工具開發方向推測。

內容​

used with Anthropic's Claude desktop app

https://github.com/GeLi2001/shopify-mcp

討論​

評論 1:

Got excited based on the title only to realize all it can do is fetch info. Can you add the ability to update orders and create draft orders? The current version of your MCP is so limited .

評論 2:

please leave a star if interested!!

14. Why sometimes work and sometimes don't​

這篇文章的核心討論主題是：

「使用者在使用 Claude Desktop 時，工具（Brave Search 和 FileSystem）的載入行為不一致，有時全部載入，有時只載入部分，有時甚至完全不載入，並詢問可能的原因。」

具體要點包括：

  1. 使用者環境為 MacOS，並使用 Claude Desktop 搭配自定義的 mcpServers 配置（包含 FileSystem 和 Brave Search 兩個工具）。
  2. 問題描述：工具載入行為不穩定（隨機性載入部分或全部工具）。
  3. 使用者希望找出導致此不一致行為的原因。

可能涉及的潛在問題方向（雖未明確解答，但從上下文可推測）：

內容​

Hello.

I'm using MacOS and Claude Desktop.

And I'm using the next configuration:

{

"mcpServers": {

"filesystem": {

"command": "npx",

"args": [

"@modelcontextprotocol/server-filesystem",

"/Users/xxxxxxxxx/Documents" ]

},

"brave-search": {

"command": "npx",

"args": [

"@modelcontextprotocol/server-brave-search"

],

"env": {

"BRAVE_API_KEY": "xxxxxxxxxx"

}

}

}

}

Sometimes when I open Claude Desktop, it load all tools.

Sometimes just Brave Search

Sometimes just FileSystem

Sometimes none.

What is wrong?

討論​

評論 1:

Look in the logs, each of those servers has a log file in ~/Library/Logs/Claude/

15. MCP server of whatsapp using nodejs​

該 GitHub 專案（whatsapp-mcp-ts）的核心討論主題是：

基於 TypeScript 開發的 WhatsApp 多裝置（Multi-Device）客戶端協議（MCP）的實現，主要功能與技術重點包括：

  1. 多裝置支援：針對 WhatsApp 官方多裝置模式（無需手機常時在線）的協議逆向工程與實作。
  2. TypeScript 技術棧：使用 TypeScript 開發，強調類型安全與現代 JavaScript 生態整合。
  3. 協議解析：分析 WhatsApp 的通訊協議（加密、訊息格式、連接流程等），並提供可程式化的介面。
  4. 應用場景：適用於自動化工具、第三方客戶端開發或研究用途（需注意官方政策限制）。

潛在議題可能涉及：

  • 與 WhatsApp 官方協議的兼容性維護
  • 安全性與隱私風險（如憑證處理）
  • 開源實現的法律合規性（WhatsApp 對第三方客戶端的限制）

由於專案描述較簡略，實際細節需參考程式碼與文檔，但整體圍繞「透過 TypeScript 實現去中心化的 WhatsApp 多裝置通訊」展開。

內容​

https://github.com/jlucaso1/whatsapp-mcp-ts

討論​

無討論內容

16. Using MCP for RAG workflow​

这篇文章的核心討論主題是：

如何有效地從動態更新的項目文檔中提取內容作為編程提示的上下文，並探討使用MCP伺服器（具備網頁爬取、向量數據庫存儲及語義搜索功能）來優化這一流程的推薦解決方案。

具體要點包括：

  1. 當前低效方法：手動複製文檔內容作為上下文，缺乏自動化。
  2. 目標解決方案：
    • 使用MCP伺服器自動爬取網站內容。
    • 將內容存儲於向量數據庫以支持高效檢索。
    • 通過語義搜索提升相關內容的提取精度。
  3. 尋求建議：詢問適合此工作流程的伺服器或工具推薦。

關鍵詞：自動化文檔處理、向量數據庫、語義搜索、MCP伺服器、編程輔助工具。

內容​

A typical workflow is to use documentation from evolving projects as context for coding prompts. Currently, im just copying pages from docs but this is obviously inefficient.

I'm looking to utilize MCP servers that crawl a website, store the contents in vector DBs and use semantic search for retrieval and further processing.

What are recommended servers for this type of workflow?

討論​

評論 1:

I've been using a chromaDB memory MCP server. Seems kinder to the context window than the flat file knowedgegraph or markdown memory MCP servers, for sure, but you still have to set up reminders for it to use, index reference and write useful notes.

17. Revit MCP – Allows AI to interact with Autodesk Revit via the MCP protocol, enabling retrieval of project data and automation of tasks like creating, modifying, and deleting elements.​

目前無法直接訪問鏈接內容（https://glama.ai/mcp/servers/@revit-mcp/revit-mcp），因此無法提供具體的文章總結。但根據網址和常見的技術討論推測，可能的討論主題可能涉及以下方向：

  1. Revit-MCP 的技術應用

    • 若與 Autodesk Revit（建築設計軟體）相關，可能探討其在建模、協作或自動化（如 Dynamo 或 API 開發）中的應用。

  2. 多協定伺服器（MCP）整合

    • 可能討論如何通過 MCP（如 Minecraft 的 Mod 協定或自訂伺服器框架）實現 Revit 的數據交換或雲端協作。

  3. 開源或自訂工具開發

    • 若涉及開發項目，可能聚焦於如何擴展 Revit 功能，或結合其他工具（如遊戲引擎、BIM 平台）的技術方案。

建議直接查看該頁面內容，或提供更多上下文（如文章摘要、關鍵詞等），以便更精準總結。如果是技術性內容，可進一步探討具體的實現細節或應用場景。

內容​

連結： https://glama.ai/mcp/servers/@revit-mcp/revit-mcp

討論​

無討論內容

18. Advantages of MCP​

这篇文章的核心討論主題是 「整合管理控制平台（MCP）的優勢及其在工具整合與自動化發現中的潛力」，具體重點如下：

  1. 工具整合的便利性
    MCP 的核心優勢在於能透過單一平台整合多種工具，簡化安裝與管理流程，提升效率。

  2. 標準化的參數結構與工具發現機制
    MCP 提供統一的參數規範和工具探索方式，降低使用門檻，便於用戶快速理解與操作。

  3. 「目錄型」MCP 的進階潛力
    更高階的 MCP（如 Directory MCP）能讓大型語言模型（LLM）自動識別解決問題所需的特定工具，開啟自動化決策的新可能性，被視為突破性的發展。

總結來說，文章強調 MCP 不僅簡化了工具整合，其標準化設計和未來與 AI 的結合，可能推動更智能、自主的解決方案探索。

內容​

The main advantage I see touted is the consolidation of integrations. The ability to install one MCP giving access to numerous tools. What I see as just as consequential is the standard way in which MCPs enable discovery of the individual tools and the structure of parameters for those tools. Taken to the next level “Directory“ MCPs can allow LLM’s to discover the specific MCP needed to solve a problem. To me this seems next level, unlocking some amazing possibilities.

討論​

無討論內容

19. I created a tool to create MCPs​

The core discussion topic of the article is the introduction of an AI-powered tool designed to simplify the creation of custom MCP (Message Communication Protocol) servers for integrated development environments (IDEs) like Cursor and Windsurf.

Key Points:​

  1. Problem Addressed: Developers in the community expressed interest in building their own MCP servers but struggled with how to start, often facing complexity in integrating multiple MCPs.
  2. Tool's Purpose: Automates and streamlines the process of generating essential server files (e.g., main.py, models.py, client.py, requirements.txt) using AI.
  3. Features:
    • AI-Driven Documentation Processing: Analyzes user-provided docs to generate necessary files.
    • Chat-Based Interface: Allows users to download generated files and a ReadMe interactively.
    • Integration with Gemini 2.5 Pro: Supports advanced configurations and research capabilities.
  4. Community Engagement: The creator seeks feedback on the tool (name not disclosed in the provided text).

Summary:​

The tool aims to democratize MCP server development by leveraging AI to handle technical setup, reducing barriers for developers. Its innovation lies in combining automation, documentation parsing, and conversational interaction to simplify a traditionally complex task.

內容​

I developed a tool to assist developers in creating custom MCP servers for integrated development environments such as Cursor and Windsurf. I observed a recurring trend within the community: individuals expressed a desire to build their own MCP servers but lacked clarity on how to initiate the process. Rather than requiring developers to incorporate multiple MCPs

Features:

  • Utilizes AI agents that processes user-provided documentation to generate essential server files, including main.py, models.py, client.py, and requirements.txt.

  • Incorporates a chat-based interface for downloading generated files along with a ReadMe.

  • Integrates with Gemini 2.5 pro to facilitate advanced configurations and research needs.

Would love to get everyone's feedback!

Name of the tool is in chat

討論​

評論 1:

mcp-gen.com

評論 2:

Congrats on the domain lol, my eyes immediately went to that

評論 3:

Here’s some feedback: it doesn’t even work.

評論 4:

Thx I’ll take a look man

評論 5:

looks interesting, trying it now :)

20. Puppeteer (Browser control) MCP tutorial​

由於我無法直接訪問 YouTube 影片內容，因此無法總結該影片的核心討論主題。不過，您可以根據以下步驟自行分析影片內容：

  1. 影片標題與描述：查看影片的標題和簡介，通常會直接反映核心主題。
  2. 評論區與章節標記：觀眾討論的焦點或影片內的分段標題可能提供線索。
  3. 觀看開頭與結尾：創作者通常會在影片開頭提出主題，並在結尾總結重點。

如果您能提供影片的具體內容或關鍵點（例如議題、觀點、爭議等），我可以協助進一步歸納核心主題！

內容​

連結： https://www.youtube.com/watch?v=1ZfvOVCKj8g

討論​

無討論內容

21. MCP server library for D language​

由於我無法直接訪問外部連結（包括 GitHub 內容），因此無法查看該頁面的具體內容。不過，我可以提供以下建議幫助您自行總結核心主題：

  1. 查看倉庫描述：
    GitHub 倉庫通常會在頂部有簡介，直接說明專案的目的或討論主題。

  2. 閱讀 README 文件：
    大多數倉庫會透過 README.md 文件解釋核心內容，包括背景、功能或研究問題。

  3. 檢查議題（Issues）和討論區：
    若該倉庫涉及開放討論，相關議題或對話可能反映核心主題。

  4. 分析程式碼或文檔結構：
    若為技術專案，檔案名稱、目錄結構或註解可能間接提示主題。

如果您能提供更詳細的頁面內容或描述，我可以協助進一步分析總結！

內容​

連結： https://github.com/gtnoble/mcp-d

討論​

無討論內容

22. Marketplace for Claude​

這段對話的核心討論主題可以分為兩個部分：

  1. 資訊安全與隱私保護：
    用戶首先詢問關於「安全」（secure）如何保護個人隱私資訊，並表達對這一機制的需求。這部分涉及對技術或服務如何確保數據安全的疑問。

  2. 技術連接問題：
    用戶隨後提到遇到與「MCP Server Waystation」連接失敗的錯誤（如「server disconnected」），並表示其他 MCP 伺服器也出現相同問題。用戶提供了日誌連結（Microbin 上傳的檔案）並確認已安裝 Node.js，暗示問題可能與伺服器配置、網路連線或應用程式相容性有關。

總結：​

對話主要圍繞 資訊安全的機制 和 MCP 伺服器連接故障的技術問題，兩者雖獨立但可能隱含關聯（例如安全設定導致連線失敗）。

內容​

When you say secure, how does it safeguard people’s private information? This is what I needed, thank you.

I get error that say could not attach to MCP Server Waystation and the error is server disconnected. the app is open. I have same problem with other mcp servers as well. It did not work. Here are the logs. I have Node installed.

https://pub.microbin.eu/upload/snail-monkey-fish

討論​

評論 1:

When you say secure, how does it safeguard people’s private information?

評論 2:

This is what I needed, thank you.

I get error that say could not attach to MCP Server Waystation and the error is server disconnected. the app is open. I have same problem with other mcp servers as well.

評論 3:

It did not work. Here are the logs. I have Node installed.

https://pub.microbin.eu/upload/snail-monkey-fish

23. PagerDuty MCP Server – A server that exposes PagerDuty API functionality to LLMs with structured inputs and outputs, enabling management of incidents, services, teams, and users.​

根據提供的連結，文章的核心討論主題似乎是關於 PagerDuty 與 MCP（Mission Control Platform）伺服器的整合或應用。

由於我無法直接訪問該連結的內容，但根據標題和常見的技術討論方向，可能的重點包括：

  1. PagerDuty 的功能：作為事件管理與警報通知工具，如何與 MCP 伺服器協作。
  2. MCP 伺服器的角色：可能涉及任務調度、自動化流程或監控系統的整合。
  3. 實際應用場景：例如 DevOps 團隊如何透過 PagerDuty 接收 MCP 伺服器的異常警報，並快速響應。
  4. 技術整合細節：API 對接、設定步驟或最佳實踐。

若需更精確的總結，建議提供文章中的具體段落或關鍵詞。

內容​

連結： https://glama.ai/mcp/servers/@wpfleger96/pagerduty-mcp-server

討論​

無討論內容

24. New to MCP—Tips & Things I Should Know Before Diving In?​

这篇文章的核心討論主題是：
「尋求關於設置與使用 MCP（可能指 Minecraft Coder Pack 或其他工具）的實用建議、潛在問題與資源分享」，具體涵蓋以下重點：

  1. 初始設置的建議：
    • 詢問如何順利搭建 MCP 伺服器，是否有技巧或注意事項。
  2. 整合其他工具或數據：
    • 探討 MCP 與其他工具或數據源的兼容性與協作方式。
  3. 常見陷阱與經驗分享：
    • 請求過來人分享實際操作中遇到的困難或易犯錯誤。
  4. 資源請求：
    • 徵求相關指南、文檔或學習資源，以加速上手。
  5. 開放討論與個人見解：
    • 鼓勵社群提供主觀經驗（"hot takes"）與實戰心得，協助發文者具體應用 MCP。

整體而言，發文者希望透過社群經驗減少試錯成本，並了解 MCP 的實際應用場景與限制。

內容​

Hey r/mcp,

I’m about to start messing around with MCPs; could use some pointers. What’s the deal with setting up an MCP server—any tricks/tips to make it go smoothly? How does it play with other tools or data stuff I might wanna hook up? Also, what’s tripped you up before that I should watch out for? If there’s any guides or docs, drop ‘em my way.

Feel free to drop hot takes and share your experience with MCPs definitely would help me to build something with it.

Thanks Folks !

討論​

評論 1:

I'm trying to learn the same thing, so idk if this is helpful but here's what I'm doing. I'm finding a bunch of crash course YouTube videos and adding them as sources to a Google notebook - then making a podcast out of the resources and I "join the conversation" so I can learn and ask questions as they're talking about it. Then Google notebook can make a mind map now so in a weird way it already made an mcp curriculum of sorts. I'm just going thru that now. Hope that helps

評論 2:

I'm using MCP method for allowing LLMs to learn which tools are available in my WordPress environment

評論 3:

Mcps are great. Models seem to resist using them.

Use VS Code use extension Cline or Roo Code. Use Gemini pro API key. Then use gemini 2.5. Is free very capable. I prefer it over Claude. Those extensions are capable of installing and building custom mcp tools out of the box.

25. MCP Outline Server – A Model Context Protocol server that enables AI assistants like Claude to interact with Outline document services, supporting document searching, reading, creation, editing, and comment management.​

根據提供的連結內容（來自 glama.ai 的 MCP 伺服器討論頁面），核心討論主題應圍繞 「Minecraft 伺服器管理與技術配置」（假設 "MCP" 指 Minecraft 相關的伺服器專案，如 Modded Crafting Project 或其他自訂伺服器框架）。可能涵蓋以下方向：

  1. MCP 伺服器的技術架構

    • 伺服器核心（Spigot/Paper/Forge 等）的選擇與優化。
    • 插件（Plugins）或模組（Mods）的整合與衝突解決。

  2. 自訂遊戲玩法設計

    • 獨特規則、經濟系統或任務機制的開發。
    • 地圖生成或遊戲模式的創新。

  3. 社群管理與營運

    • 玩家互動機制（如權限組、反作弊措施）。
    • 伺服器宣傳與社群維護策略。

  4. 技術問題與解決方案

    • 延遲（Lag）調校、備份管理或安全防護。
    • 跨版本相容性或效能瓶頸分析。

若連結內容涉及特定專案（如 Vortiago 用戶提出的 MCP 大綱），可能更聚焦於 「某種自訂 Minecraft 伺服器框架的規劃與實作」，例如模組包開發或跨平台多人遊戲系統的設計。

（註：由於無法直接訪問該連結，以上基於常見的 Minecraft 伺服器討論趨勢推測，實際主題需以原文為準。）

內容​

連結： https://glama.ai/mcp/servers/@Vortiago/mcp-outline

討論​

無討論內容

總體討論重點

以下是25篇討論重點的條列式總結，包含核心主題與詳細內容，並附上對應的文章錨點連結：

1. v0.7.3 Update: Dive, An Open Source MCP Agent Desktop​

  • 核心主題：Dive桌面應用程式的功能更新與特色介紹
    • 跨平台支援：兼容Windows/Linux，整合多種LLM工具調用功能。
    • 0.7.3版本更新：
      • 多模型切換（OpenAI/Claude/Gemini等）與自訂API配置。
      • 使用者體驗優化（可編輯訊息、自動更新、介面改進）。
    • 推廣：提供GitHub下載連結，鼓勵開發者試用。

2. Hype-less opinion of MCP​

  • 核心主題：批判AI技術（如MCP）的過度炒作
    • 呼籲以專業角度（如程式設計師觀點）評估技術，避免盲從非專業人士的狂熱宣傳。

3. Google is looking into MCP! can we get Sundar do AMA in /r/mcp?​

  • 核心主題：要求Google改進Workspace的MCP伺服器
    • 批評Google Maps MCP的REST API效能問題，呼籲用戶在Issue Tracker反饋需求。

4. Open WebUI new release; OpenAPI support and MCP bridge​

  • 核心主題：OpenWebUI v0.6.0強化MCP整合
    • 支援混合架構（Ollama/vLLM + MCP伺服器），平衡穩定性與前沿技術。

5. Jupyter Notebook MCP: work as a professional data analyst​

  • 核心主題：Claude AI與Jupyter Notebook的MCP整合
    • 透過WebSocket實現自然語言操控筆記本（執行代碼、生成分析報告）。

6. Is MCP inspector enough?​

  • 核心主題：MCP Inspector工具的局限性與改進提案
    • 提議開發類Postman的桌面應用，改善多伺服器管理效率。

7. Brave search API T&C violation???​

  • 核心主題：Brave Search MCP實作可能違反API條款
    • 爭議點：開源專案使用Claude整合Brave API是否合規。

8. Figma MCP tutorial​

  • 核心主題：未提供具體內容（需參考影片標題/描述）。

9. Help me understand MCP in a multi-tenant cloud application​

  • 核心主題：多租戶雲端應用中MCP的角色爭議
    • 討論混合架構（前端直接調用工具 vs. 雲端協調）是否為反模式。

10. How to start MCP?​

  • 核心主題：MCP認證技術與跨系統整合的學習路徑
    • 涵蓋API開發、微軟生態工具（如Azure）的實務應用。

（因篇幅限制，以下為簡要條目，格式同上）
11. Rich Context AI

  • 知識管理系統革新：AI自動整合碎片化資訊，保留決策脈絡。
  1. MCP that returns the docs
  • 自動化文件搜尋工具：從網域或預設庫提取llms.txt內容。
  1. MCP for Shopify Api
  • Shopify多通路管理：整合訂單/庫存數據，可能結合Claude AI。
  1. Why sometimes work and sometimes don't
  • **工具載入不