LinkedIn MCP | Oauth 2.0 LinkedIn connector for AI agents

CALL ANY TOOL

Get profiles and connections, retrieve company pages, list posts, and send direct messages.

linkedin_profile_get

Get profile

Retrieve the authenticated user's LinkedIn profile or a connection's profile.

Parameters

Name

Type

Required

Description

person_id

string

Optional

Person URN or vanity URL (defaults to authenticated user)

linkedin_connections_list

List connections

linkedin_company_get

Get company

linkedin_posts_list

List posts

linkedin_message_send

Send message

Build your Agent

Drop the toolkit in, point it at the user, and your social selling agent can use LinkedIn 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: ["linkedin"], toolNames: ["linkedin_profile_get", "linkedin_connections_list", "linkedin_company_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: ["linkedin"], toolNames: ["linkedin_profile_get", "linkedin_connections_list", "linkedin_company_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: ["linkedin"], toolNames: ["linkedin_profile_get", "linkedin_connections_list", "linkedin_company_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/linkedin",
    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 LinkedIn.

Connections & search

Copy the prompt

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List connections at [company].

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Search for [title] in my network.

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Find connections who changed jobs this month.

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Who are my connections at Series B companies?

Profiles & companies

Copy the prompt

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Get my LinkedIn profile.

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Get company profile for [company name].

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Who follows [company] that I'm connected to?

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Find connections with [skill] in their profile.

Outreach & posts

Copy the prompt

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Send a message to [connection]: [text].

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List my recent LinkedIn posts.

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Which posts got the most engagement this month?

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Draft an outreach message for [connection] at [company].

SEE HOW AUTH WORKS

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

1

Authorize

Your user connects

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

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

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

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 social selling 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.

LinkedIn 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 LinkedIn 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 LinkedIn oauth 2.0 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 LinkedIn?

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

What happens when a user revokes LinkedIn 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 message or view profiles outside the user's 1st-degree connections?

The agent can view public profiles but can only message 1st-degree connections. LinkedIn's API enforces connection-level access. InMail and out-of-network messaging require LinkedIn Premium and are not available via API.

LinkedIn

Tools your social selling agent reaches for on LinkedIn, scoped per user.