deadends.dev

创建者

dbwls997066 months ago

Structured failure knowledge for AI agents — 1,000+ errors across 20 domains. Dead ends, workarounds, error chains, and freshness tracking.

# error-handling

# debugging

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内容

deadends.dev

Structured failure knowledge for AI coding agents. 1,028 error entries across 20 domains.

When AI agents encounter errors, they waste tokens on approaches that are known to fail. deadends.dev tells agents what NOT to try, what actually works, what error comes next, and how fresh the data is.

8 MCP Tools

lookup_error — Match an error message against 1,028 known patterns
get_error_detail — Get full details for a specific error by ID
list_error_domains — List all 20 error domains and counts
search_errors — Fuzzy keyword search across all domains
list_errors_by_domain — List all errors in a specific domain with sorting options
batch_lookup — Look up multiple error messages at once (max 10)
get_domain_stats — Get quality metrics for a domain
get_error_chain — Traverse error transition graphs (leads_to, preceded_by)

Supported Domains (20)

python, node, docker, git, kubernetes, go, nextjs, aws, react, terraform, cuda, typescript, pip, rust, java, database, cicd, php, dotnet, networking

Key Features

Freshness tracking: Every result includes a freshness signal (fresh / aging / stale) so agents can factor data recency into decisions
Error transition graphs: leads_to, preceded_by, frequently_confused_with for multi-step debugging
Deterministic matching: Regex-based pattern matching ranked by match ratio, domain preference, and fix success rate
Schema-validated: All 1,028 canons pass strict JSON schema + business rule validation

服务器配置

{
  "mcpServers": {
    "deadend": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "deadend/deadends-dev"
      ]
    }
  }
}

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