Structured failure knowledge for AI agents — dead ends, workarounds, error chains
MCPpedia last refreshed this data
Deadends Dev MCP Server is an MCP server that structured failure knowledge for AI agents — dead ends, workarounds, error chains. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 91/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"deadend": {
"cwd": "/path/to/deadends.dev",
"args": [
"-m",
"mcp.server"
],
"command": "python"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Stop AI agents from repeating known failures — in code AND in the real world.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y '@smithery/cli' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked @smithery/cli against OSV.dev.
Click any tool to inspect its schema.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in ai-ml / developer-tools
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
A Model Context Protocol (MCP) server and CLI that provides tools for agent use when working on iOS and macOS projects.
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
MCP client bridge: connects to MCP servers and registers their tools on ctx.tools
MCP Security Weekly
Get CVE alerts and security updates for Deadends Dev MCP Server and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
Stop AI agents from repeating known failures - in code AND in the real world.
AI assistants reliably fumble two kinds of problems: known-failed code fixes, and country-specific real-world rules they've never been exposed to in training. deadends.dev now covers both:
ModuleNotFoundError, CUDA OOM, CrashLoopBackOff, etc.Why the expansion? Coding dead ends are largely solved by a good LLM. Country-specific friction - Japanese hanko requirements, Schengen 90/180 math, Ramadan business hours, Saudi alcohol ban, Indian beef taboos - is where generic AI advice breaks hardest. The codebase and schema are identical; the env segment just carries a country code.
90% Precision@1 · 0.935 MRR · Data Quality Dashboard
Website: deadends.dev · MCP Server: Smithery · PyPI: deadends-dev · API: /api/v1/index.json Repository: https://github.com/dbwls99706/deadends.dev
| Without deadends.dev | With deadends.dev |
|---|---|
Agent tries sudo pip install → breaks system Python → wastes 3 retries | Agent sees "dead end: sudo pip - fails 70%" → skips it immediately |
| Agent tells user to tip 15% at a Tokyo restaurant | Agent knows tipping is refused in Japan (culture/tipping-refused/jp) |
| Agent drafts a Thai social post referencing King Rama X | Agent stops: Article 112 lèse-majesté risk (legal/lese-majeste-article-112/th) |
| Agent fixes error A, gets confused by error B | Agent knows "A leads to B 78% of the time" → handles both |
| Agent tells unmarried couple to kiss publicly in Dubai | Agent flags UAE public decency law (legal/unmarried-public-affection/ae) |
What makes this different from asking an LLM?
{domain}/{slug}/{env} - env holds the country
code (kr, jp, us, de...) so the same taboo can be answered
differently for different jurisdictions.