io.github.pipehero/pipehero
Debug webhooks from your AI agent: inspect and replay captured webhooks on localhost.
Installation
Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).
claude mcp add --transport http pipehero 'https://mcp.pipehero.app/mcp'{
"mcpServers": {
"pipehero": {
"type": "http",
"url": "https://mcp.pipehero.app/mcp"
}
}
}Documentation
Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.
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/mcpYour 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 mcpAny 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. |
share_api_collection(collection_id, …) | The same for a whole collection: every endpoint, read-only, never its environments. |
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 installor copy SKILL.md into ~/.claude/skills/pipehero/SKILL.md.
License
MIT
Questions fréquentes
Qu'est-ce que io.github.pipehero/pipehero ?
Debug webhooks from your AI agent: inspect and replay captured webhooks on localhost.
Comment installer io.github.pipehero/pipehero ?
Il fonctionne comme serveur distant. Dans Claude Code : claude mcp add --transport http pipehero 'https://mcp.pipehero.app/mcp'. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.
Faut-il une clé API pour io.github.pipehero/pipehero ?
Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.
io.github.pipehero/pipehero est-il open source ?
Son code est public sur GitHub, mais le dépôt ne déclare pas de licence standard.
Qui maintient io.github.pipehero/pipehero ?
Il est publié par pipehero. AI.tn le référence sans le maintenir ni le recommander.
Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.