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MCP

Give your agent Bland AI tools

Every Bland AI action is exposed as an MCP tool on Pipedream's remote server. Point a client at it with your end user's ID and Connect resolves that user's Bland AI account for each tool call — you store no tokens.

Pipedream Connect

// 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": "bland_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 Call:
const result = await mcp.callTool({
  name: "bland_ai-analyze-call",
  arguments: {
    callId: "Call ID",
    goal: "Goal",
  },
})

# 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": "bland_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 Call:
        result = await session.call_tool("bland_ai-analyze-call", {
            "callId": "Call ID",
            "goal": "Goal",
        })

MCP guide →

API PROXY

Call the Bland AI API directly

For an endpoint with no pre-built tool, the Connect proxy forwards your request to the Bland AI API with the connected user's credentials attached. You store no tokens and write no refresh logic.

Pipedream Connect

const resp = await pd.proxy.get({
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  accountId: "apn_xxxxxxx",
  url: "https://api.bland.ai/v1/calls",
})

// Any allowed Bland 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://api.bland.ai/v1/calls

curl "https://api.pipedream.com/v1/connect/{project_id}/proxy/aHR0cHM6Ly9hcGkuYmxhbmQuYWkvdjEvY2FsbHM?external_user_id={external_user_id}&account_id=apn_xxxxxxx" \
  -H "Authorization: Bearer {access_token}" \
  -H "x-pd-environment: production"

API proxy guide →

SDK

Run Bland AI actions from your backend

Connect a user's Bland AI account once, then run Analyze Call on their behalf from your own code — TypeScript, Python, or plain HTTP.

Pipedream Connect

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: "bland_ai-analyze-call",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    bland_ai: { authProvisionId: "apn_xxxxxxx" },
    callId: "Call ID",
    goal: "Goal",
  },
})

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="bland_ai-analyze-call",
    external_user_id="{external_user_id}",  # any stable ID for this user in your system
    configured_props={
        "bland_ai": {"authProvisionId": "apn_xxxxxxx"},
        "callId": "Call ID",
        "goal": "Goal",
    },
)

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": "bland_ai-analyze-call",
    "configured_props": {
      "bland_ai": { "authProvisionId": "apn_xxxxxxx" },
      "callId": "Call ID",
      "goal": "Goal"
    }
  }'