# How to integrate Linkedin MCP with LlamaIndex

```json
{
  "title": "How to integrate Linkedin MCP with LlamaIndex",
  "toolkit": "Linkedin",
  "toolkit_slug": "linkedin",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/linkedin/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/linkedin/framework/llama-index.md",
  "updated_at": "2026-05-12T10:17:40.402Z"
}
```

## Introduction

This guide walks you through connecting Linkedin to LlamaIndex using the Composio tool router. By the end, you'll have a working Linkedin agent that can share a new post about our product launch, delete your last published linkedin post, fetch company pages i can manage through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Linkedin account through Composio's Linkedin MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Linkedin with

- [ChatGPT](https://composio.dev/toolkits/linkedin/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/linkedin/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/linkedin/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/linkedin/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/linkedin/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/linkedin/framework/codex)
- [Cursor](https://composio.dev/toolkits/linkedin/framework/cursor)
- [VS Code](https://composio.dev/toolkits/linkedin/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/linkedin/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/linkedin/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/linkedin/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/linkedin/framework/cli)
- [Google ADK](https://composio.dev/toolkits/linkedin/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/linkedin/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/linkedin/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/linkedin/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/linkedin/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for Linkedin
- Connect LlamaIndex to the Linkedin MCP server
- Build a Linkedin-powered agent using LlamaIndex
- Interact with Linkedin through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## What is the Linkedin MCP server, and what's possible with it?

The Linkedin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Linkedin account. It provides structured and secure access to your LinkedIn profile and company pages, so your agent can post updates, fetch your profile, manage company info, and even delete posts on your behalf.
- Automated LinkedIn posting: Let your agent create and share new posts from your profile or managed company pages, keeping your network engaged without manual effort.
- Profile information retrieval: Instantly fetch your LinkedIn profile details, including author ID and headline, for use in resumes, reporting, or personalized content generation.
- Company page management: Retrieve a list of organizations you manage, making it easy for your agent to post or gather company info for employer branding and outreach.
- Content cleanup and moderation: Direct your agent to delete specific LinkedIn posts (by share ID) to maintain a professional, up-to-date presence.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `LINKEDIN_CREATE_ARTICLE_OR_URL_SHARE` | Create article or URL share | Tool to create an article or URL share on LinkedIn using the UGC Posts API. Use when you need to share a link with optional commentary on LinkedIn. Supports sharing URLs as articles with customizable visibility settings. |
| `LINKEDIN_CREATE_COMMENT_ON_POST` | Create comment on LinkedIn post | Tool to create a first-level or nested comment on a LinkedIn share, UGC post, or parent comment via the Social Actions Comments API. Use when you need to engage with posts by adding comments or replying to existing comments. Supports text comments with optional @-mentions and image attachments. |
| `LINKEDIN_CREATE_LINKED_IN_POST` | Create a LinkedIn post | Creates a new post on LinkedIn for the authenticated user or an organization they manage. Requires w_member_social scope for posting as a person, and w_organization_social scope for posting as an organization (with ADMINISTRATOR, DIRECT_SPONSORED_CONTENT_POSTER, or CONTENT_ADMIN role). |
| `LINKEDIN_DELETE_LINKED_IN_POST` | Delete LinkedIn Post | Deletes a specific LinkedIn post (share) by its unique `share_id`, which must correspond to an existing share. |
| `LINKEDIN_DELETE_POST` | Delete Post | Delete a LinkedIn post using the Posts API REST endpoint. Supports both ugcPost and share URN formats. The endpoint is idempotent - previously deleted posts return success (204). |
| `LINKEDIN_DELETE_UGC_POST` | Delete UGC Post (Legacy) | Delete a UGC post using the legacy UGC Post API endpoint. Use when you need to delete a post using the v2/ugcPosts endpoint. Deletion is idempotent - previously deleted posts also return success. |
| `LINKEDIN_GET_AD_TARGETING_FACETS` | Get ad targeting facets | Tool to retrieve available ad targeting facets from LinkedIn Marketing API. Use when you need to discover what targeting options are available for ad campaigns (e.g., locations, industries, job functions). |
| `LINKEDIN_GET_AUDIENCE_COUNTS` | Get audience counts | Retrieves audience size counts for specified targeting criteria. Use when estimating reach for LinkedIn ad campaigns or targeted content. |
| `LINKEDIN_GET_COMPANY_INFO` | Get company info | Retrieves organizations where the authenticated user has specific roles (ACLs), to determine their management or content posting capabilities for LinkedIn company pages. |
| `LINKEDIN_GET_IMAGE` | Get image details | Tool to retrieve details of a LinkedIn image using its URN. Use when you need to check image status, get download URLs, or access image metadata for a single image. |
| `LINKEDIN_GET_IMAGES` | Get images | Tool to retrieve image metadata including download URLs, status, and dimensions from LinkedIn's Images API. Use when you need to access image details for posts, profiles, or media library assets. |
| `LINKEDIN_GET_MY_INFO` | Get my info | Fetches the authenticated LinkedIn user's profile information including name, headline, profile picture, and other profile details. |
| `LINKEDIN_GET_NETWORK_SIZE` | Get network size | Tool to retrieve the follower count for a LinkedIn organization. Use when you need to get the number of members following a specific company or organization on LinkedIn. |
| `LINKEDIN_GET_ORG_PAGE_STATS` | Get organization page statistics | Tool to retrieve page statistics for a LinkedIn organization page. Use when you need engagement metrics like page views and custom button clicks. Supports both lifetime statistics (all-time data segmented by demographics) and time-bound statistics (aggregate data for specific time ranges). Requires rw_organization_admin permission with ADMINISTRATOR role for the organization. |
| `LINKEDIN_GET_PERSON` | Get person profile | Retrieves a LinkedIn member's profile information by their person ID. Returns lite profile fields (name, profile picture) by default, or basic profile fields (including headline and vanity name) with appropriate permissions. |
| `LINKEDIN_GET_POST_CONTENT` | Get post content | Tool to retrieve detailed post content including text, images, videos, and metadata from LinkedIn by post URN. Use when you need to fetch the full content and details of a specific LinkedIn post. |
| `LINKEDIN_GET_SHARE_STATS` | Get share statistics | Retrieves share statistics for a LinkedIn organization, including impressions, clicks, likes, comments, and shares. Use to analyze content performance for an organization page. Optionally filter by time intervals to get time-bound statistics. |
| `LINKEDIN_GET_VIDEOS` | Get videos | Retrieves video metadata from LinkedIn Marketing API. Supports single video retrieval, batch retrieval (multiple videos), and finding videos by associated account with pagination. Use when you need to get video details including duration, dimensions, status, download URLs, and media library information. |
| `LINKEDIN_INITIALIZE_IMAGE_UPLOAD` | Initialize image upload | Tool to initialize an image upload to LinkedIn and return a presigned upload URL plus the resulting image URN. Use when you need to prepare an image upload for LinkedIn posts. After calling this tool, upload the image bytes to the returned upload_url via PUT request, then use the image URN in CREATE_LINKED_IN_POST action. |
| `LINKEDIN_LIST_REACTIONS` | List reactions on entity | Retrieves reactions (likes, celebrations, etc.) on a LinkedIn entity such as a share, post, or comment. Use when you need to see who reacted to content and what type of reactions were used. |
| `LINKEDIN_REGISTER_IMAGE_UPLOAD` | Register image upload | Tool to initialize a native LinkedIn image upload for feed shares and return a presigned upload URL plus the resulting digital media asset URN. Use when you need to upload an image to attach to a LinkedIn post. After calling this tool, upload the image bytes to the returned upload_url, then use the asset_urn in LINKEDIN_CREATE_LINKED_IN_POST. |
| `LINKEDIN_SEARCH_AD_TARGETING_ENTITIES` | Search ad targeting entities | Search for ad targeting entities using typeahead search. Use when you need to find targeting entities like geographic locations, job titles, industries, or other targeting criteria for LinkedIn ad campaigns. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Linkedin MCP server is an implementation of the Model Context Protocol that connects your AI agent to Linkedin. It provides structured and secure access so your agent can perform Linkedin operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A Linkedin account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Linkedin

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for Linkedin access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, linkedin)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available Linkedin tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["linkedin"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Linkedin actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Linkedin actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["linkedin"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Linkedin actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to Linkedin
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Linkedin, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["linkedin"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Linkedin actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Linkedin actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["linkedin"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Linkedin actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Linkedin to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Linkedin tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## How to build Linkedin MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/linkedin/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/linkedin/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/linkedin/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/linkedin/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/linkedin/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/linkedin/framework/codex)
- [Cursor](https://composio.dev/toolkits/linkedin/framework/cursor)
- [VS Code](https://composio.dev/toolkits/linkedin/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/linkedin/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/linkedin/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/linkedin/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/linkedin/framework/cli)
- [Google ADK](https://composio.dev/toolkits/linkedin/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/linkedin/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/linkedin/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/linkedin/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/linkedin/framework/crew-ai)

## Related Toolkits

- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active.
- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.
- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
- [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools.
- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.
- [Doppler marketing automation](https://composio.dev/toolkits/doppler_marketing_automation) - Doppler marketing automation is a platform for creating, sending, and tracking email campaigns. It helps you automate marketing workflows and manage subscriber lists for better engagement.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Linkedin MCP?

With a standalone Linkedin MCP server, the agents and LLMs can only access a fixed set of Linkedin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Linkedin and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Linkedin tools.

### Can I manage the permissions and scopes for Linkedin while using Tool Router?

Yes, absolutely. You can configure which Linkedin scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Linkedin data and credentials are handled as safely as possible.

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)