Second Opinion MCP Server is an AI-powered server designed to assist developers in solving coding problems by aggregating insights from multiple sources such as Google's Gemini AI, Stack Overflow accepted answers, and Perplexity AI analysis. It provides detailed solutions, automatic language detection, code snippet extraction, markdown report generation, and Git-aware file context gathering.
This server helps developers solve complex coding issues by leveraging AI models and community knowledge. It combines multiple expert sources into one solution, providing comprehensive problem-solving assistance with best practices, performance optimization tips, and error handling recommendations.
Developers, software engineers, and technical teams who face challenges in debugging, optimizing, or implementing features in their codebase can benefit from this tool. It’s especially useful for those working with frameworks like React and technologies like WebSockets.
The project is hosted on GitHub under the repository PoliTwit1984/second-opinion-mcp-server. You can access its source code, documentation, and setup instructions there.
Use this tool when encountering difficult coding problems that require expert-level insights, debugging assistance, or optimization strategies. It’s ideal for situations where traditional approaches have failed or when you need a second opinion validated by AI and trusted resources.
To set up the server, install dependencies using 'npm install', build it with 'npm run build', and configure environment variables (GEMINI_API_KEY, PERPLEXITY_API_KEY, STACK_EXCHANGE_KEY) in the MCP settings file.
The get_second_opinion tool requires at least the 'goal' parameter describing what you’re trying to accomplish. Optional parameters include 'error', 'code', 'solutionsTried', and 'filePath'.
While some functionalities may work anonymously, full functionality requires valid API keys for Google's Gemini AI, Perplexity AI, and optionally Stack Exchange.
Yes, the server automatically detects the programming language based on file extensions and provides contextual solutions accordingly.
The tool is primarily intended for development and debugging purposes. For production use, ensure proper testing and validation of generated solutions before deployment.
MCP（Model Context Protocol，模型上下文協議）是一個開放協議，旨在標準化應用程式如何為大型語言模型（LLM）提供上下文資訊。類似於 AI 應用的「USB-C 端口」，MCP 確保 AI 模型能與各種資料來源和工具無縫連接。
MCP Server 是支援 MCP 協議的伺服器，能以標準化方式在應用程式與 AI 模型之間交換上下文資訊。它為開發者提供了一個便捷的方式，將 AI 模型與資料庫、API 或其他資料來源整合。
MCP Server 通過統一管理 AI 模型與多種資料來源的連接，消除了開發自訂適配器的複雜性。無論是開發者、資料科學家還是 AI 應用建置者，MCP Server 都能簡化整合流程，節省時間與資源。
MCP Server 作為中間橋樑，將來自各種資料來源的上下文資訊轉換為 AI 模型能理解的格式。通過遵循 MCP 協議，它確保資料在應用程式與 AI 模型之間以標準化方式傳輸。
在 mcpserver.shop 上，您可以瀏覽我們的 MCP Server 目錄。目錄按行業（如金融、醫療、教育）分類，每款伺服器皆附有詳細說明與標籤，幫助您快速找到符合需求的選項。
mcpserver.shop 上的 MCP Server 目錄可免費瀏覽。但部分伺服器由第三方提供商託管，可能涉及使用費用。請查看各伺服器的詳細頁面以了解具體資訊。
MCP Server 支援多種資料來源，包括資料庫、API、雲端服務及自訂工具。MCP 協議的靈活性使其能將幾乎任何類型的資料來源連接到 AI 模型。
MCP Server 主要面向開發者、資料科學家與 AI 應用建置者。然而，mcpserver.shop 提供了詳細的文件與指南，幫助不同技術水平的用戶輕鬆上手。
是的，MCP 是一個開源協議，鼓勵社群參與與合作。如需了解更多細節或參與貢獻，請造訪 MCP 官方文件。
在 mcpserver.shop 上，每款 MCP Server 的詳細頁面皆包含提供商的聯絡資訊或連結。您可直接聯繫提供商以獲取更多詳情或技術支援。