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,模型上下文协议)是一个开放协议,旨在标准化应用程序如何为大型语言模型(LLM)提供上下文信息。类似于 AI 应用的'USB-C 端口',MCP 确保 AI 模型能够与各种数据源和工具无缝连接。
MCP 服务器是支持 MCP 协议的服务器,能够以标准化的方式在应用程序和 AI 模型之间交换上下文信息。它为开发者提供了一种便捷的方式,将 AI 模型与数据库、API 或其他数据源集成。
MCP 服务器通过统一管理 AI 模型与多种数据源的连接,消除了开发自定义适配器的复杂性。无论是开发者、数据科学家还是 AI 应用构建者,MCP 服务器都能简化集成过程,节省时间和资源。
MCP 服务器充当中间桥梁,将来自各种数据源的上下文信息转化为 AI 模型能够理解的格式。通过遵循 MCP 协议,它确保数据在应用程序和 AI 模型之间以标准化方式传输。
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MCP 服务器支持多种数据源,包括数据库、API、云服务和自定义工具。MCP 协议的灵活性使其能够连接几乎任何类型的数据源到 AI 模型。
MCP 服务器主要面向开发者、数据科学家和 AI 应用构建者。不过,mcpserver.shop 提供了详细的文档和指南,帮助不同技术水平的用户轻松上手。
是的,MCP 是一个开源协议,鼓励社区参与和协作。如需了解更多细节或参与贡献,请访问 MCP 官方文档。
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