Pipehero

Pipehero tạo đường hầm từ localhost của bạn đến một URL công khai và ghi lại mọi webhook, để bạn có thể kiểm tra, phát lại và gỡ lỗi chúng — kể cả từ tác nhân AI của bạn qua MCP.

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Pipehero — MCP server + skill

Pipehero tunnels your localhost to a public URL and captures every webhook, so you can inspect, replay, and debug them — including from your AI agent via MCP.

This repo is auto-published from Pipehero's source. It holds the public MCP manifest and the agent skill. Issues/PRs welcome.

🌐 Website: https://pipehero.app
📚 Docs: https://pipehero.app/docs

Connect the MCP server

Let an agent (Claude Code, Cursor, Copilot, Windsurf, Zed, …) list your tunnels, read captured webhooks, and replay them to localhost.

Remote — recommended (no install, OAuth)

claude mcp add --transport http pipehero https://mcp.pipehero.app/mcp

Your client opens the browser to sign in and approve — no token to paste.

For CI/headless, pass a token from https://pipehero.app/dashboard/settings:

claude mcp add --transport http pipehero https://mcp.pipehero.app/mcp \
  --header "Authorization: Bearer <token>"

Local — via the CLI

curl -fsSL https://pipehero.app/install | sh
pipehero login
claude mcp add pipehero -- pipehero mcp

Any MCP client works — add it to .cursor/mcp.json, claude_desktop_config.json, etc.:

{ "mcpServers": { "pipehero": { "command": "pipehero", "args": ["mcp"] } } }

Tools

Tool	What it does
`list_tunnels`	Your tunnels and whether each is online.
`list_requests(subdomain)`	Recent captured webhooks for a tunnel.
`get_request(subdomain, id)`	Full request + response (headers + body).
`replay_request(subdomain, id)`	Replay a webhook to your localhost.
`start_tunnel(name, port)`	Expose a local port on a public URL (local MCP only).
`stop_tunnel(name)`	Stop a tunnel started with `start_tunnel` (local MCP only).
`list_api_collections`, `get_api_collection(collection_id)`	Your saved API docs: folders, endpoints, environments (remote MCP, and local from 0.1.14).
`get_api_endpoint(collection_id, method + path)`	One endpoint in full, with its version and response examples.
`upsert_api_endpoint(collection_id, method, path, …)`	Create or update an endpoint by route, so docs follow your code.
`add_api_example(collection_id, method + path, name, status, …)`	Save a response example.
`delete_api_endpoint(collection_id, method + path)`	Remove an endpoint whose route is gone.
`import_api_spec(format, content)` / `export_api_collection(collection_id)`	OpenAPI, Postman and curl, both ways.
`run_api_endpoint(collection_id, method + path, environment)`	Send it and read the real response.
`save_captured_request(collection_id, tunnel, request_id)`	Turn a real captured request into docs, without credentials.
`check_api_drift(collection_id, tunnel)`	Compare the docs with real traffic (Pro/Team).
`list_api_monitors(collection_id)`	Which endpoints are monitored and which are failing, with the last error (Pro/Team).
`list_api_proposals`	What happened to your proposed changes, on workspaces that review AI changes.

Because your agent has both the captured webhook and your codebase, it can explain why a handler failed — then fix it and replay to confirm.

Skill

Install the skill so your agent knows when and how to use Pipehero:

pipehero skill install

or copy SKILL.md into ~/.claude/skills/pipehero/SKILL.md.

License

MIT