Hivemind
Provides instant access to a searchable knowledge base of 16,000+ community-driven troubleshooting solutions for common coding problems. Includes community feedback and smart ranking to help AI assistants find the most effective solutions.
README
hivemind
Instant troubleshooting solutions for AI coding assistants Community-driven knowledge base with 16,000+ solutions and growing
Works with any MCP-compatible CLI: Claude Code, Codex CLI, Gemini CLI, Grok CLI, Cursor, and more.
π― What is hivemind?
hivemind is a Model Context Protocol (MCP) server that gives AI coding assistants instant access to a searchable knowledge base of troubleshooting solutions.
Supported platforms:
- Claude Code (Anthropic)
- Codex CLI (OpenAI)
- Gemini CLI (Google)
- Grok CLI (xAI)
- Cursor
- Any MCP-compatible tool
When you hit an error, search hivemind - get ranked solutions from the community in seconds.
β¨ Features
- π Full-text search - Fast Postgres FTS with relevance ranking
- π Community feedback - Say "hivemind: worked" to rate solutions
- π― Trigger words - AI automatically tracks your feedback
- π Smart ranking - Solutions sorted by success rate and votes
- π Zero config - No API keys, no setup, just install and use
- β‘ Fast - < 1s search response time
π¦ Installation
Quick Install
Claude Code:
npm install -g hivemind-mcp
claude mcp add hivemind -- npx hivemind-mcp
Codex CLI:
npm install -g hivemind-mcp
codex mcp add hivemind -- npx -y hivemind-mcp
Gemini CLI:
npm install -g hivemind-mcp
gemini mcp add hivemind npx -y hivemind-mcp
Cursor / Windsurf:
npm install -g hivemind-mcp
# Add to MCP settings: npx hivemind-mcp
Manual Setup (Claude Code)
Option A: Via Claude Code UI
- Open Claude Code settings
- Go to MCP Servers section
- Add new server:
- Name:
hivemind - Command:
npx - Args:
["hivemind-mcp"]
- Name:
Option B: Via config file
Edit ~/.config/claude/mcp_config.json:
{
"mcpServers": {
"hivemind": {
"command": "npx",
"args": ["hivemind-mcp"]
}
}
}
Restart your CLI/editor to activate the MCP server.
π Usage
Search for Solutions
User: "MCP connection refused"
AI: [searches hivemind automatically]
Or explicitly:
User: "Search hivemind for playwright timeout"
Give Feedback
After trying a solution:
User: "hivemind: worked"
Or:
User: "hivemind: failed"
Your AI automatically tracks your feedback to improve rankings!
π Example Queries
| Problem | Query |
|---|---|
| MCP won't connect | "MCP connection refused" |
| Playwright issues | "playwright click not working" |
| Claude hooks | "hooks don't fire" |
| Auth problems | "user signed in but gets must sign in error" |
| Supabase issues | "supabase migration failed" |
Current knowledge base: 16,000+ solutions (and growing) covering MCP, Playwright, Supabase, web development, databases, security, and more.
π€ Contributing Solutions
Have a solution that helped you? Share it with the community!
Via MCP Tool
User: "I want to contribute a solution"
Claude: [guides you through contribution form]
Via Email
Send to: [your-email] (Beta testers will get this in welcome message)
Include:
- Problem/error message
- Category (mcp-troubleshooting, web-automation, etc.)
- Step-by-step solutions
- What to check before/after
- Common mistakes
π How Trigger Words Work
When you say "hivemind: worked" or "hivemind: failed":
- Your AI recognizes the trigger phrase
- Automatically calls feedback tool
- Backend increments thumbs_up or thumbs_down
- Future searches show better-ranked solutions
Why trigger words? Natural language ("that worked") is ambiguous. Trigger phrases ensure your AI ALWAYS catches your feedback.
π‘οΈ Infrastructure
Rate Limits:
- Search: 100 requests/hour per IP
- Voting: 20 votes/hour per IP
- Contributions: 5/hour per IP
Stack:
- Backend: Supabase (Postgres 17 + Edge Functions)
- Database: 16,000+ solutions
- Security: IP banning, input sanitization, moderated contributions
π Security
hivemind is designed to be a safe, public knowledge base. We take security seriously:
Credential Scanning
All contributions are scanned for sensitive data before storage. Our scanner blocks:
- API Keys: OpenAI, Anthropic, AWS, GCP, Azure, Stripe, Slack, GitHub, GitLab, and 30+ providers
- Tokens: JWT, Bearer tokens, OAuth tokens, PATs
- Secrets: Database passwords, webhook URLs, Basic auth headers
- Obfuscation Attempts: Base64-encoded secrets, zero-width character tricks
How it works:
- Pattern matching against 45+ known secret formats
- Base64 decode + rescan for hidden secrets
- Entropy analysis for high-randomness strings near keywords like
key=,secret= - Zero-width character stripping to defeat obfuscation
If your contribution is rejected, check for accidentally included credentials.
Row-Level Security
All database tables use Postgres RLS policies. The public API can only:
- Read solutions and skills
- Submit contributions (to moderation queue)
- Submit feedback votes
No direct write access to production data.
Rate Limiting
Aggressive rate limits prevent abuse:
- Search: 100/hour
- Voting: 20/hour
- Contributions: 5/hour
What We Don't Store
- No user accounts required
- No tracking cookies
- No personally identifiable information
- IP addresses used only for rate limiting (not logged with searches)
See PRIVACY.md for full privacy policy.
π Monitoring
Beta testers: We monitor usage via Supabase dashboard. You can check your own usage:
# View your recent searches (if you clone the repo)
source ~/.claude/scripts/turso-helpers.sh
db-stats
See MONITORING.md for admin dashboard access.
πΊοΈ Roadmap
Completed:
- β Core search and feedback
- β Trigger word system
- β Rate limiting
- β Security (IP bans, sanitization, moderation queue)
- β 16,000+ solutions
- β Version update notifications
Coming Soon:
- Semantic search (AI embeddings)
- Contributor leaderboard
- Browser extension
- Dangerous command blocklist
See ROADMAP.md for full details.
π§ Technical Details
Stack:
- MCP Server: TypeScript + @modelcontextprotocol/sdk
- Backend: Supabase (Postgres 17 + Deno Edge Functions)
- Search: Postgres Full-Text Search (ts_rank)
- Hosting: Supabase free tier + npm registry
Architecture:
User β Any AI CLI/Editor β MCP β Supabase Edge Functions β Postgres
Public Source:
hivemind-mcp/- MCP client package (what you install via npm)
π Documentation
- INSTALL.md - Detailed installation guide
- TRIGGER_FEEDBACK_SYSTEM.md - How feedback works
- MONITORING.md - Admin monitoring guide
- ROADMAP.md - Product roadmap
β Troubleshooting
MCP not loading
# Check if installed
npm list -g hivemind-mcp
# Reinstall
npm install -g hivemind-mcp@latest
# Restart your AI CLI/editor
Search not working
- Check your AI CLI MCP logs
- Verify internet connection
- Try: "Search hivemind for test"
Feedback not tracked
Known issue - tracking endpoint works but MCP may not call it consistently. We're debugging this.
π Reporting Issues
- Check existing issues
- Create new issue with:
- What you tried
- What happened
- Your AI CLI (Claude Code, Codex, Gemini, etc.)
- Operating system
π License
MIT License - see LICENSE file
π Star History
If hivemind helped you, give it a star β
Built with β€οΈ for the AI coding community
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.