How to integrate Peopledatalabs MCP with Pydantic AI

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Introduction

This guide walks you through connecting Peopledatalabs to Pydantic AI 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', find people with 'data scientist' in new york through natural language commands.

This guide will help you understand how to give your Pydantic AI 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.

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Peopledatalabs
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Peopledatalabs workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 & Triggers

Tools
Autocomplete field suggestionsProvides autocompletion suggestions for a specific field (e.
Clean company dataCleans 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.
Clean location dataCleans and standardizes a raw, unformatted location string into a structured representation, provided the input is a recognizable geographical place.
Clean school dataCleans and standardizes school information; provide at least one of the school's name, website, or profile for optimal results.
Person Search with ElasticsearchPerforms a search for person profiles within people data labs using a custom elasticsearch domain specific language (dsl) query.
Enrich Company DataEnriches company data from people data labs with details like firmographics and employee counts, requiring at least one company identifier.
Enrich IP DataEnriches an ip address with company, location, metadata, and person data from people data labs.
Enrich job title dataEnhances a job title by providing additional contextual information and details.
Enrich person dataEnriches 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.
Enrich skill dataRetrieves 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.
Generate Search QueryConverts natural language queries into structured pdl elasticsearch queries for people or company searches; generates optimized query structure without executing the search.
Get column detailsRetrieves predefined enum values for a column name from `enum mappings.
Get schemaRetrieves the schema, including field names, descriptions, and data types, for 'person' or 'company' entity types.
Identify person dataRetrieves detailed profile information for an individual from people data labs (pdl), requiring at least one identifier such as email, phone, profile url, name, or company.
People Search with ElasticsearchSearches for person profiles in the people data labs (pdl) database using an elasticsearch domain specific language (dsl) query.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Peopledatalabs
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Peopledatalabs
  • MCPServerStreamableHTTP connects to the Peopledatalabs MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Peopledatalabs
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["peopledatalabs"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
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 session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
peopledatalabs_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[peopledatalabs_mcp],
    instructions=(
        "You are a Peopledatalabs assistant. Use Peopledatalabs tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Peopledatalabs endpoint
  • The agent uses GPT-5 to interpret user commands and perform Peopledatalabs operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Peopledatalabs.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Peopledatalabs API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Peopledatalabs and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Peopledatalabs
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["peopledatalabs"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    peopledatalabs_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[peopledatalabs_mcp],
        instructions=(
            "You are a Peopledatalabs assistant. Use Peopledatalabs tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Peopledatalabs.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Peopledatalabs through Composio's Tool Router. With this setup, your agent can perform real Peopledatalabs actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Peopledatalabs for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Peopledatalabs MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Peopledatalabs MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Peopledatalabs MCP with Claude Agent SDK

Google ADK

Google ADK

Use Peopledatalabs MCP with Google ADK

LangChain

LangChain

Use Peopledatalabs MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Peopledatalabs MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Peopledatalabs MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Peopledatalabs MCP with LlamaIndex

CrewAI

CrewAI

Use Peopledatalabs MCP with CrewAI

FAQ

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 Pydantic AI?

Yes, you can. Pydantic AI 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.

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