# How to integrate Fullenrich MCP with LangChain

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

## Introduction

This guide walks you through connecting Fullenrich to LangChain 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 LangChain 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

- [OpenAI Agents SDK](https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk)
- [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)
- [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
- Connect your Fullenrich project to Composio
- Create a Tool Router MCP session for Fullenrich
- Initialize an MCP client and retrieve Fullenrich tools
- Build a LangChain agent that can interact with Fullenrich
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## 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

No description provided.

### 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 dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Fullenrich tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Fullenrich 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
- This approach allows the agent to dynamically load and use Fullenrich tools as needed
```python
# Create Tool Router session for Fullenrich
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['fullenrich']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['fullenrich']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "fullenrich-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "fullenrich-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Fullenrich related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Fullenrich related question or task to the agent.\n");

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

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;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

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

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['fullenrich']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "fullenrich-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Fullenrich related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

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

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['fullenrich']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "fullenrich-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Fullenrich related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\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

You've successfully built a LangChain agent that can interact with Fullenrich through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Fullenrich MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk)
- [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)
- [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

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- [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.
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- [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 LangChain?

Yes, you can. LangChain 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.

---
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