# How to integrate Linkedin MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Linkedin MCP with Vercel AI SDK v6", "toolkit": "Linkedin", "toolkit_slug": "linkedin", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/linkedin/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/linkedin/framework/ai-sdk.md", "updated_at": "2026-05-12T10:17:40.402Z" } ``` ## Introduction This guide walks you through connecting Linkedin to Vercel AI SDK v6 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 Vercel AI SDK 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) - [Mastra AI](https://composio.dev/toolkits/linkedin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/linkedin/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/linkedin/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Linkedin integration - Using Composio's Tool Router to dynamically load and access Linkedin tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## 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: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client What's happening: - We're creating a Tool Router session that gives your agent access to Linkedin tools - The create method takes the user ID and specifies which toolkits should be available - The returned mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all Linkedin-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["linkedin"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available Linkedin tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to linkedin, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use Linkedin tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["linkedin"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to linkedin, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Linkedin agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## 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) - [Mastra AI](https://composio.dev/toolkits/linkedin/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/linkedin/framework/llama-index) - [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 Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)