# How to integrate Peopledatalabs MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Peopledatalabs MCP with Vercel AI SDK v6", "toolkit": "Peopledatalabs", "toolkit_slug": "peopledatalabs", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/peopledatalabs/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/peopledatalabs/framework/ai-sdk.md", "updated_at": "2026-05-12T10:21:44.689Z" } ``` ## Introduction This guide walks you through connecting Peopledatalabs to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Peopledatalabs agent that can enrich this email with full person profile, standardize and clean this company name, get detailed info for the skill 'python' through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Peopledatalabs account through Composio's Peopledatalabs MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Peopledatalabs with - [OpenAI Agents SDK](https://composio.dev/toolkits/peopledatalabs/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/peopledatalabs/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/peopledatalabs/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/peopledatalabs/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/peopledatalabs/framework/codex) - [OpenClaw](https://composio.dev/toolkits/peopledatalabs/framework/openclaw) - [Hermes](https://composio.dev/toolkits/peopledatalabs/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/peopledatalabs/framework/cli) - [Google ADK](https://composio.dev/toolkits/peopledatalabs/framework/google-adk) - [LangChain](https://composio.dev/toolkits/peopledatalabs/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/peopledatalabs/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/peopledatalabs/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/peopledatalabs/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Peopledatalabs integration - Using Composio's Tool Router to dynamically load and access Peopledatalabs 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 Peopledatalabs MCP server, and what's possible with it? The Peopledatalabs MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Peopledatalabs account. It provides structured and secure access to rich B2B data, so your agent can enrich profiles, standardize company details, validate customer information, and perform advanced searches with ease. - Comprehensive person data enrichment: Automatically enhance individual profiles using identifiers like email, phone, or full name combined with company or location data. - Company data validation and enrichment: Instantly verify and enrich company details with firmographics, employee counts, and standardized fields to power your workflows. - Advanced person search and filtering: Leverage Elasticsearch-powered queries to find the exact professional profiles you need using job title, skills, experience, and more. - Data cleaning and standardization: Cleanse and structure raw company, school, or location data to maintain high-quality records in your systems. - Skill and job title enrichment: Provide context and standardized information for job titles or professional skills to improve analytics and targeting. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PEOPLEDATALABS_CLEAN_COMPANY_DATA` | Clean company data | Cleans and standardizes company information based on a name, website, or profile URL; providing at least one of these inputs is highly recommended for meaningful results. | | `PEOPLEDATALABS_CLEAN_COMPANY_POST` | Clean company data (POST) | Tool to clean and standardize company data using POST method. Use when you need to standardize company information by providing company name, website, or social profile. Returns standardized company information including name, website, LinkedIn profile, and other company identifiers. | | `PEOPLEDATALABS_CLEAN_LOCATION_DATA` | Clean location data | Cleans and standardizes a raw, unformatted location string into a structured representation, provided the input is a recognizable geographical place. | | `PEOPLEDATALABS_CLEAN_LOCATION_POST` | Clean location data (POST) | Tool to clean and standardize location data using POST method. Use when you need to normalize raw location strings into structured location information including city, region, and country. | | `PEOPLEDATALABS_CLEAN_SCHOOL_DATA` | Clean school data | Cleans and standardizes school information; provide at least one of the school's name, website, or profile for optimal results. | | `PEOPLEDATALABS_CLEAN_SCHOOL_DATA_POST` | Clean school data (POST) | Tool to clean and standardize school data using POST method. Use when you need to clean school information by providing name, website, or profile. | | `PEOPLEDATALABS_ENRICH_BULK_COMPANY_DATA` | Enrich Bulk Company Data | Tool to enrich up to 100 companies in a single request using the Bulk Company Enrichment API. Use when you need to enrich multiple company profiles efficiently. Each request must include at least one company identifier (website, profile, name, ticker, or pdl_id). Results are returned in the same order as the input requests, with individual status codes indicating success (200) or failure (404). | | `PEOPLEDATALABS_ENRICH_BULK_PERSON_DATA` | Enrich bulk person data | Tool to enrich up to 100 person profiles in a single API request using the Bulk Person Enrichment API. Use when you need to enrich multiple people efficiently, as this effectively increases the rate limit by up to 100x compared to individual enrichment calls. Each request in the array can use the same parameters as the single person enrichment endpoint. | | `PEOPLEDATALABS_ENRICH_COMPANY_DATA` | Enrich Company Data | Enriches company data from People Data Labs with details like firmographics and employee counts. CRITICAL: This action REQUIRES at least one company identifier. DO NOT send empty {} requests. You MUST provide at least one of: pdl_id, name, profile, ticker, or website. Valid request examples: - {"name": "Apple Inc."} - enrich by company name - {"website": "google.com"} - enrich by website URL - {"ticker": "MSFT"} - enrich by stock ticker - {"profile": "linkedin.com/company/microsoft"} - enrich by social profile. Each call consumes API credits; use specific identifiers rather than exploratory requests. | | `PEOPLEDATALABS_ENRICH_IP_DATA` | Enrich IP Data | Enriches an IP address with company, location, metadata, and person data from People Data Labs. | | `PEOPLEDATALABS_ENRICH_JOB_TITLE_DATA` | Enrich job title data | Enhances a job title by providing additional contextual information and details. | | `PEOPLEDATALABS_ENRICH_PERSON_DATA` | Enrich person data | Enriches person data using various identifiers; requires a primary ID (profile, email, phone, email_hash, lid, pdl_id) OR a name (full, or first and last) combined with another demographic detail (e.g., company, school, location). | | `PEOPLEDATALABS_ENRICH_SKILL_DATA` | Enrich skill data | Retrieves detailed, standardized information for a given skill by querying the People Data Labs Skill Enrichment API; for best results, provide a recognized professional skill or area of expertise. | | `PEOPLEDATALABS_GENERATE_SEARCH_QUERY` | Generate Search Query | Converts natural language queries into structured PDL Elasticsearch queries for people or company searches; generates optimized query structure without executing the search. | | `PEOPLEDATALABS_AUTOCOMPLETE_FIELD_SUGGESTIONS` | Autocomplete field suggestions | Provides autocompletion suggestions for a specific field (e.g., company, skill, title) based on partial text input. | | `PEOPLEDATALABS_GET_AUTOCOMPLETE_SUGGESTIONS_POST` | Get autocomplete suggestions (POST) | Tool to get autocompletion suggestions using POST method for complex query parameters. Use when building type-ahead interfaces or needing to suggest values for Search API queries. Supports company, location, skill, title, and other fields with configurable result size. | | `PEOPLEDATALABS_GET_COLUMN_DETAILS` | Get column details | Retrieves predefined enum values for a column name from `enum_mappings.json`; `is_enum` in the response will be false if the column is not found or is not an enum type. | | `PEOPLEDATALABS_GET_SCHEMA` | Get schema | Retrieves the schema, including field names, descriptions, and data types, for 'person' or 'company' entity types. | | `PEOPLEDATALABS_GET_SUBJECT_REQUESTS` | Get subject requests | Tool to retrieve subject access requests for data privacy compliance. Use when you need to manage or review data subject requests related to person data in your PeopleDataLabs account. | | `PEOPLEDATALABS_IDENTIFY_PERSON_DATA` | Identify person data | Retrieves detailed profile information for an individual from People Data Labs (PDL), requiring at least one identifier such as email, phone, or profile URL. If using name alone, it must be paired with at least one additional attribute (company, location, school, etc.) — name-only queries return no match. | | `PEOPLEDATALABS_PEOPLE_SEARCH_ELASTIC` | People Search with Elasticsearch | Searches for person profiles in the People Data Labs (PDL) database using an Elasticsearch Domain Specific Language (DSL) query. This action allows for highly targeted searches based on criteria such as job titles, skills, company details, location, experience, and more. Preconditions: - The provided Elasticsearch query (in the `query` field) must be a syntactically correct JSON object representing a valid Elasticsearch query. - The query must utilize fields that are defined in the People Data Labs person schema. - The `dataset` parameter must specify one of the allowed dataset categories. | | `PEOPLEDATALABS_QUERY_PERSON_CHANGELOG` | Query person changelog | Tool to query the changelog of person records between two consecutive dataset versions. Returns information about updates, additions, deletions, merges, and opt-outs for individuals. Use when you need to track changes to person profiles across PDL dataset versions or monitor specific person IDs for updates. | | `PEOPLEDATALABS_SEARCH_COMPANY_ELASTIC` | Company Search with Elasticsearch | Performs a search for company profiles within People Data Labs using a custom Elasticsearch Domain Specific Language (DSL) query. This action allows for detailed and complex filtering based on various attributes of a company, such as name, industry, employee_count, founded year, location, and more. Results can be paginated using the `size` and `scroll_token` parameters. Preconditions: - The `query` parameter must contain a valid Elasticsearch DSL query string, structured as a JSON object. - This action queries the People Data Labs company search endpoint (`/v5/company/search`) and returns company records. | | `PEOPLEDATALABS_SEARCH_COMPANY_POST` | Search Company Records (POST) | Tool to search and filter company records from the full Company Dataset using Elasticsearch or SQL queries via POST method. Use when you need to find multiple companies matching specific criteria with complex filtering. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Peopledatalabs MCP server is an implementation of the Model Context Protocol that connects your AI agent to Peopledatalabs. It provides structured and secure access so your agent can perform Peopledatalabs 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 Peopledatalabs 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 Peopledatalabs-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: ["peopledatalabs"], }); 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 Peopledatalabs 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 peopledatalabs, 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 Peopledatalabs 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: ["peopledatalabs"], }); 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 peopledatalabs, 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 Peopledatalabs 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 Peopledatalabs MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/peopledatalabs/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/peopledatalabs/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/peopledatalabs/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/peopledatalabs/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/peopledatalabs/framework/codex) - [OpenClaw](https://composio.dev/toolkits/peopledatalabs/framework/openclaw) - [Hermes](https://composio.dev/toolkits/peopledatalabs/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/peopledatalabs/framework/cli) - [Google ADK](https://composio.dev/toolkits/peopledatalabs/framework/google-adk) - [LangChain](https://composio.dev/toolkits/peopledatalabs/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/peopledatalabs/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/peopledatalabs/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/peopledatalabs/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. - [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Peopledatalabs MCP? With a standalone Peopledatalabs MCP server, the agents and LLMs can only access a fixed set of Peopledatalabs tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Peopledatalabs 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 Peopledatalabs tools. ### Can I manage the permissions and scopes for Peopledatalabs while using Tool Router? Yes, absolutely. You can configure which Peopledatalabs 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 Peopledatalabs data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)