Build with Google Vertex AI

// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": "{project_id}",
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "google_vertex_ai",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// e.g. run Analyze Image/Video:
const result = await mcp.callTool({
  name: "google_vertex_ai-analyze-image-video",
  arguments: {
    projectId: "Project ID",
    instructions: "Instructions",
  },
})

# access_token: mint a short-lived token with the Connect SDK — see the MCP guide
headers = {
    "Authorization": f"Bearer {access_token}",
    "x-pd-project-id": "{project_id}",
    "x-pd-environment": "production",
    "x-pd-external-user-id": "{external_user_id}",  # any stable ID for this user in your system
    "x-pd-app-slug": "google_vertex_ai",
}

async with streamablehttp_client("https://remote.mcp.pipedream.net/v3", headers=headers) as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await session.list_tools()

        # e.g. run Analyze Image/Video:
        result = await session.call_tool("google_vertex_ai-analyze-image-video", {
            "projectId": "Project ID",
            "instructions": "Instructions",
        })

const resp = await pd.proxy.get({
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  accountId: "apn_xxxxxxx",
  url: "https://www.googleapis.com/oauth2/v1/userinfo",
})

// Any allowed Google Vertex AI endpoint works here. Pipedream attaches the
// connected account's credentials to the outgoing request.

# The path segment is the target URL, URL-safe base64 encoded:
# https://www.googleapis.com/oauth2/v1/userinfo

curl "https://api.pipedream.com/v1/connect/{project_id}/proxy/aHR0cHM6Ly93d3cuZ29vZ2xlYXBpcy5jb20vb2F1dGgyL3YxL3VzZXJpbmZv?external_user_id={external_user_id}&account_id=apn_xxxxxxx" \
  -H "Authorization: Bearer {access_token}" \
  -H "x-pd-environment: production"

import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "google_vertex_ai-analyze-image-video",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    google_vertex_ai: { authProvisionId: "apn_xxxxxxx" },
    projectId: "Project ID",
    instructions: "Instructions",
  },
})

from pipedream import Pipedream

pd = Pipedream(
    client_id="{oauth_client_id}",
    client_secret="{oauth_client_secret}",
    project_id="{project_id}",
    project_environment="production",
)

result = pd.actions.run(
    id="google_vertex_ai-analyze-image-video",
    external_user_id="{external_user_id}",  # any stable ID for this user in your system
    configured_props={
        "google_vertex_ai": {"authProvisionId": "apn_xxxxxxx"},
        "projectId": "Project ID",
        "instructions": "Instructions",
    },
)

curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "google_vertex_ai-analyze-image-video",
    "configured_props": {
      "google_vertex_ai": { "authProvisionId": "apn_xxxxxxx" },
      "projectId": "Project ID",
      "instructions": "Instructions"
    }
  }'