Lusha MCP connector for AI agents

Tools your agent reaches for on Lusha, scoped per user.

CALL ANY TOOL

Search, enrich, and find lookalike B2B contacts and companies, billed to the requesting user.

lushamcp_account_usage

Retrieve account credit balance, rate-limit status, plan inf

Retrieve account credit balance, rate-limit status, plan info, and per-action credit pricing.

Parameters

Name

Type

Required

Description

conversation_id

string

Optional

Conversation correlation ID. Present only when an earlier tool response in this conversation returned one; that value is carried unchanged on subsequent calls. Omitted on the first call.

reason_for_invocation

string

Optional

Brief explanation of why you chose this tool for the current task. Optional audit field; max 500 characters (longer values are truncated). Plain text only.

lushamcp_companies_search

Look up known companies in the lusha database by name, domai

lushamcp_contacts_search

Look up a known business contact in lusha by name, company,

lushamcp_lookalike_companies

Discover companies similar to a set of seed companies, retur

lushamcp_lookalike_contacts

Discover contacts similar to a set of seed contacts, returni

lushamcp_prospecting_company_enrich

Reveal full firmographic details for one or more lusha compa

lushamcp_prospecting_company_filters

Resolve valid filter values accepted by the company prospect

lushamcp_prospecting_company_search

Find companies by firmographic filters such as industry, siz

lushamcp_prospecting_contact_enrich

Reveal emails and phone numbers for one or more lusha contac

lushamcp_prospecting_contact_filters

Resolve valid filter values accepted by the contact prospect

lushamcp_prospecting_contact_search

Find business contacts by filters such as job title, seniori

lushamcp_prospecting_search_guide

Return a step-by-step guide for structuring lusha prospectin

Build your Agent

Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.

from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="lushamcp")

mcp = MultiServerMCPClient({
    "lushamcp": {
        "url": "https://mcp.scalekit.com/lushamcp",
        "headers": {"Authorization": "Bearer " + token}
    }
})
tools = await mcp.get_tools()

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "lushamcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/lushamcp
// Pass: Authorization: Bearer + token

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "lushamcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/lushamcp
// Pass: Authorization: Bearer + token

from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="lushamcp")
# Connect to MCP at https://mcp.scalekit.com/lushamcp
# Pass: Authorization: Bearer + token

Try these prompts

Copy any prompt into your agent. Each maps directly to a Lusha tool. Click to copy, paste into your agent, done.

Get started

Copy the prompt

Copied

Resolve contacts by LinkedIn URL, email, or name and return their recent activity signals?

Copy the prompt

Copied

Return recent activity signals (promotions, company changes) for known Lusha contact IDs?

Advanced

Copy the prompt

Copied

Return available contact signal types accepted by contacts signals tools?

Copy the prompt

Copied

Reveal emails and phone numbers for one or more Lusha contact IDs?

SEE HOW AUTH WORKS

Your users connect once. Their Lusha credentials stay vaulted, every call is scope-checked, and every action is logged.

Authorize

Your user connects

Lusha MCP

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

Store

Their

Lusha MCP

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

Resolve

When your agent calls a

Lusha MCP

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

Audit

Every

Lusha MCP

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

See the same per-user auth pattern across other connectors.

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

GTM

HubSpot to Slack updates agent

Watch HubSpot deal stage changes and post structured updates to the right Slack channel. Reps stop checking the CRM all day.

HubSpot

Slack

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 identity, scope enforcement, and audit. Connectors follow.

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.

// shared token
 audit → bot_service_account

 // scalekit
 audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

One connector today. Ten 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 Lusha 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 Lusha 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 Lusha?
Yes. Pass a tool name filter to listScopedTools so the marketing agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Lusha.

What happens when a user revokes Lusha 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.

Whose Lusha credits do contact enrichments spend?
The authorizing user's. Contact and company searches, lookalikes, and intent signals draw from that user's Lusha quota, so prospecting spend maps to the rep who triggered it.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit