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Second Opinion MCP Server: Rapid, Data-Driven Second Opinions - MCP Implementation

Second Opinion MCP Server: Rapid, Data-Driven Second Opinions

Second Opinion MCP Server delivers rapid, data-driven second opinions for critical decisions, boosting confidence in high-stakes environments – trusted by experts worldwide.

Research And Data
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96% of users reported increased productivity after just one week

About Second Opinion MCP Server

What is Second Opinion MCP Server: Rapid, Data-Driven Second Opinions?

Second Opinion MCP Server is an advanced developer tool designed to accelerate problem-solving by providing rapid, evidence-based recommendations. It combines real-time analysis from Gemini AI, community-vetted solutions from Stack Overflow, and contextual insights from Perplexity AI to deliver actionable code reviews and debugging strategies. This server automates the process of cross-referencing multiple data sources, ensuring developers receive comprehensive guidance tailored to their specific coding challenges.

How to use Second Opinion MCP Server: Rapid, Data-Driven Second Opinions?

  1. Install dependencies via the provided setup script
  2. Configure API credentials in the environment file
  3. Invoke the get_second_opinion endpoint with your code snippet and error context
  4. Receive structured recommendations including code modifications, testing strategies, and maintenance best practices
  5. Review the generated markdown report for implementation steps and validation procedures

Second Opinion MCP Server Features

Key Features of Second Opinion MCP Server: Rapid, Data-Driven Second Opinions?

  • Multi-source analysis: Synthesizes AI predictions with community knowledge and technical documentation
  • Context-aware processing: Automatically detects programming languages and parses Git history for version-specific insights
  • Actionable outputs: Provides code-ready solutions with inline edits and unit test suggestions
  • Continuous validation: Includes automated checks for solution compatibility and edge case coverage
  • Custom reporting: Generates detailed markdown reports with version tracking and dependency analysis

Use Cases of Second Opinion MCP Server: Rapid, Data-Driven Second Opinions?

Real-Time Debugging

Instantly analyze error logs alongside live code to identify root causes and suggest patches

Codebase Audits

Perform automated code reviews for security vulnerabilities and performance bottlenecks

Framework Migration

Generate transition guides between legacy systems and modern frameworks using historical pattern analysis

Documentation Generation

Create API references and usage examples based on existing code implementations

Collaborative Troubleshooting

Share analysis results across teams with version-controlled solution tracking

Second Opinion MCP Server FAQ

FAQ: Second Opinion MCP Server

Does the server support all programming languages?

Current implementation covers major languages including Python, JavaScript, Java, C#, and Go. Support for additional languages is added via community contributions.

How is solution reliability ensured?

Proposed fixes are validated against 3+ data sources and scored by confidence level. Critical changes require manual approval before implementation.

Can it integrate with CI/CD pipelines?

Yes, provides REST API endpoints and CLI tools for automated deployment validation and pre-commit checks.

What are the system requirements?

Runs on Linux/macOS/Windows systems with Python 3.8+ and 4GB+ RAM. GPU acceleration recommended for large-scale analysis.

Content

Second Opinion MCP Server

An MCP server that provides AI-powered assistance for coding problems by combining insights from:

  • Google's Gemini AI
  • Stack Overflow accepted answers
  • Perplexity AI analysis

Features

  • Get detailed solutions for coding problems with context from multiple sources
  • Automatic language detection from file extensions
  • Code snippet extraction and formatting
  • Markdown report generation for solutions
  • Git-aware file context gathering

Setup

  1. Install dependencies:
npm install
  1. Build the server:
npm run build
  1. Configure environment variables in MCP settings:
{
  "mcpServers": {
    "second-opinion": {
      "command": "node",
      "args": ["/path/to/second-opinion-server/build/index.js"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key",
        "PERPLEXITY_API_KEY": "your-perplexity-api-key",
        "STACK_EXCHANGE_KEY": "your-stack-exchange-key"
      }
    }
  }
}

Required environment variables:

  • GEMINI_API_KEY: Google's Gemini AI API key
  • PERPLEXITY_API_KEY: Perplexity AI API key
  • STACK_EXCHANGE_KEY: Stack Exchange API key (optional, uses anonymous access if not provided)

Usage

The server provides a single tool:

get_second_opinion

Get AI-powered insights and solutions for coding problems.

Input Schema:

{
  "goal": "string (required) - What you're trying to accomplish",
  "error": "string (optional) - Any error messages you're seeing",
  "code": "string (optional) - Relevant code context",
  "solutionsTried": "string (optional) - What solutions you've already tried",
  "filePath": "string (optional) - Path to the file with the issue"
}

Example Input:

{
  "goal": "Fix React useEffect cleanup error",
  "error": "React Hook useEffect has a missing dependency: 'socket'. Either include it in the dependency array, or remove the dependency array  react-hooks/exhaustive-deps",
  "code": "useEffect(() => {\n  socket.on('message', handleMessage);\n  return () => socket.off('message');\n}, []);",
  "solutionsTried": "I tried adding socket to the dependency array but then it causes infinite re-renders"
}

Example Response:

Understanding the Problem

The core issue lies in how React's useEffect hook manages side effects and their dependencies. When you provide an empty dependency array ([]), you're telling React that the effect should only run once when the component mounts and that the cleanup function should run when the component unmounts.

However, in your code, the socket object is used within the effect. This creates a closure where the handleMessage function and the cleanup function (socket.off('message')) implicitly depend on the current value of socket. If socket ever changes (e.g., due to a reconnection or reinitialization), your effect will be using an outdated socket instance.

Common Issues

  • Stale Closures: The event handler (handleMessage) might be working with an old socket object
  • Incorrect Cleanup: The socket.off('message') might not remove the intended event listener
  • Memory Leaks: Failed cleanup can lead to memory leaks and unexpected behavior

Solutions

1. Using useRef for Stable References

import React, { useEffect, useRef } from 'react';

function MyComponent({ socket }) {
  const socketRef = useRef(socket);

  useEffect(() => {
    const currentSocket = socketRef.current;
    currentSocket.on('message', handleMessage);
    return () => {
      currentSocket.off('message');
    };
  }, []); // Empty dependency array is now safe
}

Why This Works:

  • useRef creates a mutable object that persists across renders
  • The ref object itself doesn't change, preventing effect re-runs
  • You can access the latest socket value via the ref

2. Using useCallback for Handlers

import React, { useEffect, useRef, useCallback } from 'react';

function MyComponent({ socket, someProp }) {
  const socketRef = useRef(socket);

  const handleMessage = useCallback((message) => {
    // Do something with the message and potentially someProp
    console.log(message, someProp);
  }, [someProp]); // Include dependencies of handleMessage

  useEffect(() => {
    const currentSocket = socketRef.current;
    currentSocket.on('message', handleMessage);
    return () => {
      currentSocket.off('message', handleMessage);
    };
  }, [handleMessage]); // Depend on the memoized handler
}

3. Managing Socket Lifecycle Inside useEffect

import React, { useEffect, useState } from 'react';

function MyComponent() {
  const [messages, setMessages] = useState([]);

  useEffect(() => {
    const socket = new WebSocket('your_websocket_url');

    const handleMessage = (message) => {
      setMessages((prevMessages) => [...prevMessages, message]);
    };

    socket.on('message', handleMessage);

    return () => {
      socket.off('message', handleMessage);
      socket.close();
    };
  }, []); // Empty array is correct - socket lifecycle managed within
}

4. Custom Hook Solution

function useSocket(url) {
  const [messages, setMessages] = useState([]);
  const socketRef = useRef(null);

  useEffect(() => {
    const socket = new WebSocket(url);
    socketRef.current = socket;

    const handleMessage = (message) => {
      setMessages((prevMessages) => [...prevMessages, message]);
    };

    socket.on('message', handleMessage);

    return () => {
      socket.off('message', handleMessage);
      socket.close();
    };
  }, [url]);

  const sendMessage = useCallback((message) => {
    if (socketRef.current?.readyState === WebSocket.OPEN) {
      socketRef.current.send(message);
    }
  }, []);

  return { messages, sendMessage };
}

Best Practices

  1. Dependency Management
* Use `useRef` for stable references
* Memoize handlers with `useCallback`
* Consider socket lifecycle management
  1. Performance Optimization
* Minimize unnecessary re-renders
* Handle high-volume messages efficiently
* Use appropriate cleanup patterns
  1. Error Handling
* Handle connection errors gracefully
* Implement reconnection logic if needed
* Clean up resources properly
  1. Testing Considerations
* Mock WebSocket connections in tests
* Verify event listener cleanup
* Test error scenarios

Project Structure

src/
├── config.ts        # Configuration and API settings
├── fileUtils.ts     # File operations and language detection
├── index.ts         # Entry point
├── perplexity.ts   # Perplexity AI integration
├── server.ts       # MCP server implementation
├── stackOverflow.ts # Stack Overflow API integration
└── types.ts        # TypeScript interfaces

Known Issues

See errors.md for current issues and workarounds.

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