Deadends.Dev
Give your agent structured records of known dead ends, workarounds, and error chains so it stops repeating failed fixes.
Overview
dev.deadends/deadends-dev is an MCP server for the Operate phase that serves structured failure knowledge—dead ends, workarounds, and error chains—for AI coding agents.
What is this MCP server?
- Structured failure knowledge: dead ends, workarounds, and linked error chains
- Built for AI agents that otherwise retry the same broken approaches
- Local stdio MCP via PyPI package deadends-dev v0.3.2
- Open source at github.com/dbwls99706/deadends.dev with site deadends.dev
- Complements generic web search with agent-oriented troubleshooting lore
- PyPI package deadends-dev version 0.3.2
- stdio MCP transport via PyPI registry
- Open-source repository github.com/dbwls99706/deadends.dev
What problem does it solve?
Agents waste tokens re-trying fixes that the ecosystem already knows are dead ends, with no shared failure memory in the session.
Who is it for?
Solo builders who lean on agents for integration debugging and want a dedicated failure-knowledge MCP beside generic search.
Skip if: Teams that only need greenfield scaffolding with no recurring production or integration pain, or who require proprietary incident data stays on-prem only with no external corpus.
What do I get? / Deliverables
Your agent can pull structured workarounds and error-chain context so debugging moves past known traps faster.
- Agent-queryable dead ends, workarounds, and error-chain entries from deadends.dev
- Faster convergence on fixes that avoid documented failure paths
Recommended MCP Servers
Journey fit
Failure knowledge pays off in Operate when production bugs, flaky integrations, and repeated agent loops need a shared memory of what already failed. Errors subphase is where you triage incidents and narrow root cause—deadends.dev supplies curated failure patterns instead of rediscovering them each session.
How it compares
Curated failure-knowledge MCP, not a live log aggregator or generic documentation crawler.
Common Questions / FAQ
Who is dev.deadends/deadends-dev for?
Developers using AI agents who repeatedly hit the same library, cloud, or toolchain failures and want structured dead-end and workaround data.
When should I use dev.deadends/deadends-dev?
Use it in Operate (and late Ship) when debugging error chains and you want the agent to consult known failures before proposing another doomed fix.
How do I add dev.deadends/deadends-dev to my agent?
Install the PyPI package deadends-dev (v0.3.2) and register it as an stdio MCP server in Claude Code, Cursor, or your compatible client.