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Meta MCP

Execute a workflow as a structured tool for AI Agents

Meta MCP

AI-generated

Summary

Calls an HTTP webhook URL with a chosen method (GET, POST, PUT, DELETE) for each input item and returns the JSON response, so a workflow can be exposed as a structured tool for AI Agents.

Inputs

  • method (required) — HTTP method: GET (default), POST, PUT or DELETE.
  • url (required) — Webhook URL to call. Leading/trailing whitespace and a leading '/' are stripped. Supports <> placeholders, replaced with values from the injected Bonnie context (AsyncLocalStorage); missing/null values become empty strings.
  • authentication — None (default), Bearer Auth (httpBearerAuth credential, sent as 'Authorization: Bearer ') or Header Auth (httpHeaderAuth credential, sent as a custom header name/value).
  • bodyJson (required) — JSON body, shown only for POST and PUT. Supports <> placeholders and is parsed with JSON.parse, so invalid JSON causes an error. Defaults to {}.
  • meta — Additional structured data/schema describing the tool. Declared in the UI but not read by the execute code.
  • curlImport — UI helper for importing a cURL command to fill in the fields.

Output shape

A list of items, with one item per response object. If the response is a JSON array, each element becomes its own item. Otherwise the whole response becomes a single item's json.

Sends an Accept: application/json header and requests a JSON response. A body is sent only for POST/PUT. With Continue On Fail enabled, a failed item yields {json:{error:message}}. Otherwise the node throws a NodeApiError.

Examples

Example 1: Trigger an external n8n webhook workflow from an AI Agent with a JSON payload

Method=POST, URL=https://example.com/webhook/abc, Authentication=Header Auth, Body={"query":"<>"}

Example 2: Fetch data from a protected endpoint

Method=GET, URL=https://api.example.com/items, Authentication=Bearer Auth

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