# How to integrate Fullenrich MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Fullenrich MCP with OpenAI Agents SDK",
  "toolkit": "Fullenrich",
  "toolkit_slug": "fullenrich",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:12:26.439Z"
}
```

## Introduction

This guide walks you through connecting Fullenrich to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Fullenrich agent that can enrich this list of leads with emails and phones, check your fullenrich credit balance right now, get the latest status of bulk enrichment job through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Fullenrich account through Composio's Fullenrich MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Fullenrich with

- [Claude Agent SDK](https://composio.dev/toolkits/fullenrich/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/fullenrich/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/fullenrich/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/fullenrich/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/fullenrich/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/fullenrich/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/fullenrich/framework/cli)
- [Google ADK](https://composio.dev/toolkits/fullenrich/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/fullenrich/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/fullenrich/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/fullenrich/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/fullenrich/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/fullenrich/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Fullenrich
- Configure an AI agent that can use Fullenrich as a tool
- Run a live chat session where you can ask the agent to perform Fullenrich operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

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

The Fullenrich MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fullenrich account. It provides structured and secure access to powerful B2B contact enrichment features, so your agent can perform actions like preparing contact lists, starting bulk enrichment jobs, retrieving batch results, and monitoring credit usage on your behalf.
- Prepare and validate contact data lists: Guide your agent to create properly formatted lists of lead information for bulk enrichment, ensuring accuracy and readiness for processing.
- Launch bulk enrichment jobs: Let your agent start large-scale enrichment tasks for up to 100 contacts at a time, aggregating verified emails and phone numbers from multiple vendors.
- Retrieve bulk enrichment results: Automatically check on the status of ongoing jobs and fetch enriched contact data as soon as it's ready, streamlining your lead generation workflows.
- Monitor workspace credit balance: Enable your agent to check your current API credit usage so you always know how many enrichment requests you have remaining.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FULLENRICH_CREATE_CONTACT_DATA_LIST` | Create Contact Data List | Tool to create a list of contact data entries. Use when preparing the 'datas' payload for bulk enrichment; validates each contact's composition and returns a JSON-ready list. |
| `FULLENRICH_GET_CURRENT_CREDIT_BALANCE` | Get current credit balance | Tool to retrieve current workspace credit balance. Use after authenticating your API key. |
| `FULLENRICH_FULLENRICH_GET_ENRICHMENT_RESULT` | Get Bulk Enrichment Result | Tool to retrieve results of a bulk enrichment by enrichment ID. Use after submitting a bulk enrichment job to check its status and get enriched data. |
| `FULLENRICH_GET_REVERSE_EMAIL_RESULT` | Get Reverse Email Result | Tool to retrieve results from a reverse email lookup operation using reverse email ID. Use after submitting a reverse email lookup to check its status and get contact data. |
| `FULLENRICH_REVERSE_EMAIL_LOOKUP` | Reverse Email Lookup | Tool to perform bulk reverse email lookup to retrieve full person and company profile from work or personal email addresses. Use when you have email addresses and need to enrich them with complete contact information. Results are processed asynchronously; use the returned enrichment_id to retrieve actual data. |
| `FULLENRICH_SEARCH_COMPANY` | Search Company | Tool to search for companies based on filters including name, domain, industry, type, headquarters location, headcount, and founded year. Multiple filters within the same field are combined with OR logic. Use when you need to find companies matching specific criteria. |
| `FULLENRICH_SEARCH_PEOPLE` | Search People | Tool to search for people based on filters including company, location, skills, position titles, and seniority levels. Multiple filters within the same field are combined with OR logic. Use when you need to find people matching specific professional criteria. |
| `FULLENRICH_START_BULK_ENRICHMENT` | Start Bulk Enrichment | Tool to start a bulk enrichment job. Use when you have up to 100 contacts prepared and need batch enrichment. Use after confirming contact data. |
| `FULLENRICH_VERIFY_API_KEY` | Verify API Key | Tool to check if your API key is valid and return the associated workspace ID. Use when you need to verify API key validity before performing other operations. |

## Supported Triggers

None listed.

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

The Fullenrich MCP server is an implementation of the Model Context Protocol that connects your AI agent to Fullenrich. It provides structured and secure access so your agent can perform Fullenrich 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 starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Fullenrich project
- Some knowledge of Python or Typescript

### 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).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Fullenrich.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only fullenrich.
- The router checks the user's Fullenrich connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Fullenrich.
- This approach keeps things lightweight and lets the agent request Fullenrich tools only when needed during the conversation.
```python
# Create a Fullenrich Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["fullenrich"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Fullenrich
const session = await composio.create(userId as string, {
  toolkits: ['fullenrich'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Fullenrich. "
        "Help users perform Fullenrich operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Fullenrich. Help users perform Fullenrich operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["fullenrich"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Fullenrich. "
        "Help users perform Fullenrich operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['fullenrich'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Fullenrich. Help users perform Fullenrich operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Fullenrich MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Fullenrich.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Fullenrich MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/fullenrich/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/fullenrich/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/fullenrich/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/fullenrich/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/fullenrich/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/fullenrich/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/fullenrich/framework/cli)
- [Google ADK](https://composio.dev/toolkits/fullenrich/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/fullenrich/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/fullenrich/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/fullenrich/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/fullenrich/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/fullenrich/framework/crew-ai)

## Related Toolkits

- [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools.
- [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations.
- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
- [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates.
- [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations.
- [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code.
- [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding.
- [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls.
- [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.
- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [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.
- [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.
- [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses.

## Frequently Asked Questions

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

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

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Fullenrich tools.

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

Yes, absolutely. You can configure which Fullenrich 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 Fullenrich data and credentials are handled as safely as possible.

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