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Second Opinion MCP Server

MCP Server

AI-powered coding help from Gemini, Stack Overflow, and Perplexity

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Updated Jan 14, 2025

About

Provides context-aware code solutions by aggregating insights from Google Gemini, Stack Overflow accepted answers, and Perplexity AI. It detects language, extracts code snippets, formats Markdown reports, and integrates with Git for file context.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Overview

The Second Opinion MCP Server is a specialized AI assistant designed to help developers troubleshoot and resolve coding problems by synthesizing insights from multiple authoritative sources. It pulls together the generative power of Google Gemini, contextual knowledge from Stack Overflow’s accepted answers, and analytical summaries from Perplexity AI. By combining these perspectives, the server delivers comprehensive, actionable guidance that goes beyond a single source’s limitations.

This tool is especially valuable for teams and individual developers who need rapid, reliable help while maintaining code quality. Rather than sifting through forums or waiting for a mentor’s reply, the server presents a structured markdown report that includes the problem description, error analysis, potential pitfalls, and concrete code snippets. Developers can integrate this server into their IDE workflows or continuous‑integration pipelines, allowing the assistant to surface insights automatically whenever a build fails or a linter flags an issue.

Key capabilities include:

  • Multi‑source reasoning: The server queries Gemini for generative explanations, retrieves relevant Stack Overflow posts via the API, and uses Perplexity to distill long‑form content into concise insights.
  • Contextual awareness: It detects the programming language from file extensions, extracts relevant code blocks, and formats them for readability.
  • Git integration: When a file path is provided, the server can pull surrounding commits or branch information to give richer context.
  • Markdown output: All responses are rendered as markdown, making them ready for inclusion in documentation or issue trackers.

Typical use cases involve debugging complex framework bugs (e.g., React hook warnings), refactoring legacy code, or learning new libraries. A developer can simply invoke the tool with a brief description of their goal, any error messages, and the current code snippet. The assistant then returns a curated report that not only fixes the immediate issue but also explains underlying concepts, potential edge cases, and best‑practice recommendations.

Because it aggregates knowledge from multiple curated sources, the server offers a unique advantage: it mitigates the risk of relying on a single, potentially outdated or incomplete answer. Developers can trust that the guidance reflects both community consensus and cutting‑edge AI reasoning, making it a powerful addition to any modern development stack.