How do I install Pipehero?

Pipehero is a remote plugin. You do not need to install anything locally. Add the server URL to your AI app's MCP configuration and you are ready to go.

Is Pipehero safe to use?

Yes. Pipehero passed MCP Marketplace's automated security scan with a score of 10/10 (low risk). Every server on MCP Marketplace is security-scanned before it's listed; see the full security report on this page for the findings and permissions.

What AI apps work with Pipehero?

Pipehero uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Pipehero MCP Server

by Pipehero

Developer ToolsLow Risk10.0MCP RegistryRemote

Free

Server data from the Official MCP Registry

Debug webhooks from your AI agent: inspect and replay captured webhooks on localhost.

About

Debug webhooks from your AI agent: inspect and replay captured webhooks on localhost.

Remote endpoints: streamable-http: https://mcp.pipehero.app/mcp

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.

4 tools verified · Open access · No issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Found in Source Code

Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.

HTTP Network Access

Connects to external APIs or services over the internet.

How to Connect

Remote Plugin

No local installation needed. Your AI client connects to the remote endpoint directly.

Add this to your MCP configuration to connect:

{
  "mcpServers": {
    "io-github-pipehero-pipehero": {
      "url": "https://mcp.pipehero.app/mcp"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

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.
`get_api_share_link(collection_id, method + path)`	The endpoint's shareable link: a read-only page with its docs, examples and latest run.
`share_api_endpoint(collection_id, method + path, …)`	Create that link (workspace-only by default), open it to anyone with the link, or invite people by email.

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

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