What this error means

Error: connect ECONNREFUSED 127.0.0.1:3000 / TCPConnectWrap.afterConnect [as oncomplete] is a Claude Code failure pattern reported for developers trying to user configures an mcp server in claude code and receives econnrefused because the server hasn't finished listening yet; needs fix for startup race condition and localhost ipv6 binding.. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Multiple sources confirm: startdebugging.net (May 2026), claudecodeguides.com. Error occurs when local MCP stdio/http server starts before client connection is ready. Race between node:net listener and client TCP connect. Covers IPv6 ::1 vs 127.0.0.1 mismatch. Also related to MCP server stdio hang when launched from Claude Code (stdout pollution, npx install prologue). Strong signal from GitHub issues #34982 (CLI MCP connection issue) and community guides. Already published: Cursor 'model not available' and 'OpenAI API key not working' but NOT ECONNREFUSED from local MCP servers.

Common causes

  • Multiple sources confirm: startdebugging.net (May 2026), claudecodeguides.com. Error occurs when local MCP stdio/http server starts before client connection is ready. Race between node:net listener and client TCP connect. Covers IPv6 ::1 vs 127.0.0.1 mismatch. Also related to MCP server stdio hang when launched from Claude Code (stdout pollution, npx install prologue). Strong signal from GitHub issues #34982 (CLI MCP connection issue) and community guides. Already published: Cursor 'model not available' and 'OpenAI API key not working' but NOT ECONNREFUSED from local MCP servers.

Quick fixes

  1. Confirm the exact error signature matches Error: connect ECONNREFUSED 127.0.0.1:3000 / TCPConnectWrap.afterConnect [as oncomplete].
  2. Check the Claude Code account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.