PhantomBuster

Live

API KEY

GROWTH AUTOMATION

Automation

Every LinkedIn scraper, email finder, and outreach automation your growth team runs lives in PhantomBuster. PhantomBuster MCP gives your agent authenticated access to automations scoped to the user who authorized it.

  • Acts as the user: Access and write actions stay tied to the PhantomBuster account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: Permissions enforced. 90-day audit trail.
PhantomBuster
agent · Acme Q3
Run
Run the LinkedIn company scraper for [target company list] and return emails found.
S
phantom_agent_launch
2.1s
Growth automation agent
Phantom launched. 47 profiles scraped from 3 companies. 31 emails found: 18 direct, 13 guessed pattern. CSV result ready for CRM import.
Sources: LinkedIn scraper, 3 companies
phantombustermcp
1 phantom
18:29
Message Claude...

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

CALL ANY TOOL
List, launch, and monitor PhantomBuster automations, retrieve output data, and check quota.
phantombuster_list_save
List save
Create or update a lead list in PhantomBuster organization storage, defined by a name and a filter over stored leads. Provide id to update an existing list, or omit it to create a new one.
Parameters
Name
Type
Required
Description
filter
object
Required
Filter criteria defining which stored leads belong to this list, as a JSON object keyed by field name, e.g. {"jobTitle": {"operator": "contains", "valueToCompare": "Founder"}}. Supports "and"/"or" composition and a "__global_search__" key for a global keyword search.
name
string
Required
Name of the lead list.
description
string
Optional
Description of the lead list.
id
string
Optional
ID of an existing lead list to update. Omit to create a new list.
tags
array
Optional
Tags to associate with this lead list.
phantombuster_list_delete
List delete
phantombuster_list_fetch
List fetch
phantombuster_ai_task
Ai task
phantombuster_org_fetch
Org fetch
phantombuster_ai_advice
Ai advice
phantombuster_agent_save
Agent save
phantombuster_leads_save
Leads save
phantombuster_agent_stop
Agent stop
phantombuster_branch_diff
Branch diff
phantombuster_agent_fetch
Agent fetch
phantombuster_location_ip
Location ip
phantombuster_script_save
Script save
phantombuster_agent_delete
Agent delete
phantombuster_agent_launch
Agent launch
phantombuster_script_fetch
Script fetch
phantombuster_branch_create
Branch create
phantombuster_branch_delete
Branch delete
phantombuster_ai_completions
Ai completions
phantombuster_script_delete
Script delete
phantombuster_branch_release
Branch release
phantombuster_companies_save
Companies save
phantombuster_container_fetch
Container fetch
phantombuster_companies_search
Companies search
phantombuster_container_attach
Container attach
phantombuster_identities_search
Identities search
phantombuster_user_fetch_me
User fetch me
phantombuster_icps_fetch_all
Icps fetch all
phantombuster_user_update_me
User update me
phantombuster_leads_save_many
Leads save many
Build your Agent
Drop the toolkit in, point it at the user, and your growth automation agent can use PhantomBuster from the first run.
Python · LlamaIndex
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 });
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 }],
});
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
Copied
List all available phantoms.
Copy the prompt
Copied
Launch [phantom name] with argument [value].
Copy the prompt
Copied
What is the status of phantom [id]?
Copy the prompt
Copied
Get latest output from [phantom name].
Results & data
Copy the prompt
Copied
Return all emails from the last LinkedIn scraper run.
Copy the prompt
Copied
Get CSV output from [phantom] last run.
Copy the prompt
Copied
How many rows did [phantom] return yesterday?
Copy the prompt
Copied
List all phantoms that ran today.
Quota & monitoring
Copy the prompt
Copied
How much quota have I used this month?
Copy the prompt
Copied
Which phantoms consumed the most quota?
Copy the prompt
Copied
List phantoms that failed in the last 7 days.
Copy the prompt
Copied
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.
GTM and RevOps Teams
Outbound prospecting agent
Searches Apollo for prospects matching your ICP, scores and ranks them, drafts personalized outreach in Gmail, and logs every send to Google Sheets. Mail goes out as the rep, not from a shared inbox.
GTM and RevOps Teams
CRM AI agent
Reads the Granola transcript after every call, extracts next steps and updates the HubSpot record, drafts the follow-up in Gmail, and confirms in Slack, all on the rep's own delegated OAuth.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
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
Search Apollo for ICP matches, rank them, draft personalised Gmail outreach, and log every send to Google Sheets.
GTM
CRM AI agent
Turn each Granola call transcript into a HubSpot record update, a drafted Gmail follow-up, and a Slack recap.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
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.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""phantombuster"": {
""url"": ""https://mcp.scalekit.com/phantombuster"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.phantombuster]
url = ""https://mcp.scalekit.com/phantombuster""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""phantombuster"": {
""url"": ""https://mcp.scalekit.com/phantombuster"",
""type"": ""http""
}
}
}