PhantomBuster MCP | API Key PhantomBuster connector for AI agents

CALL ANY TOOL

List, launch, and monitor PhantomBuster automations, retrieve output data, and check quota.

phantom_agents_list

List phantoms

List all PhantomBuster agents (automations) in the account.

Parameters

Name

Type

Required

Description

limit

integer

Optional

Max agents to return

phantom_agent_launch

Launch phantom

phantom_agent_get

Get phantom status

phantom_result_get

Get output

phantom_containers_list

List containers

Build your Agent

Drop the toolkit in, point it at the user, and your growth automation agent can use PhantomBuster from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["phantombuster"], toolNames: ["phantom_agents_list", "phantom_agent_launch", "phantom_agent_get"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["phantombuster"], toolNames: ["phantom_agents_list", "phantom_agent_launch", "phantom_agent_get"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["phantombuster"], toolNames: ["phantom_agents_list", "phantom_agent_launch", "phantom_agent_get"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/phantombuster",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using PhantomBuster.

Launch & run

Copy the prompt

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List all available phantoms.

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Launch [phantom name] with argument [value].

Copy the prompt

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What is the status of phantom [id]?

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Get latest output from [phantom name].

Results & data

Copy the prompt

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Return all emails from the last LinkedIn scraper run.

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Get CSV output from [phantom] last run.

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How many rows did [phantom] return yesterday?

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List all phantoms that ran today.

Quota & monitoring

Copy the prompt

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How much quota have I used this month?

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Which phantoms consumed the most quota?

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List phantoms that failed in the last 7 days.

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Get container quota breakdown.

SEE HOW AUTH WORKS

Users authorize PhantomBuster once. Their credentials stay vaulted, every call is checked, and every action is logged.

1

Authorize

Your user connects

PhantomBuster

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

2

Store

Their

PhantomBuster

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

3

Resolve

When your agent calls a

PhantomBuster

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

4

Audit

Every

PhantomBuster

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other growth automation agents and MCP connectors. Working code, live demos, fork what fits.

SALES

Outbound prospecting agent

Build targeted prospect lists with Apollo, enrich with firmographic data, and draft personalised outreach. Runs on a schedule.

Apollo

View agent

SALES

Sales call prep agent

Pull Granola notes and Attio contact history to draft a pre-call brief before every sales meeting. Zero rep input.

Granola

Attio

View agent

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

PhantomBuster today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access PhantomBuster as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the PhantomBuster api key stored?

In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in PhantomBuster?

Yes. Pass a tool name filter to listScopedTools so the growth automation agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches PhantomBuster.

What happens when a user revokes PhantomBuster access?

The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Can the agent access phantoms owned by other team members?

Only phantoms visible under the authorizing user's API key. PhantomBuster keys are account-scoped. Shared team containers are accessible if the key has workspace-level access.

PhantomBuster

Tools your growth automation agent reaches for on PhantomBuster, scoped per user.