Pipehero

Pipehero 将你的本地主机隧道到公共 URL，并捕获每一个 webhook，让你可以检查、重放和调试它们——包括通过 MCP 从你的 AI 智能体进行操作。

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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