# How to integrate Recruitee MCP with LangChain

```json
{
  "title": "How to integrate Recruitee MCP with LangChain",
  "toolkit": "Recruitee",
  "toolkit_slug": "recruitee",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/recruitee/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/recruitee/framework/langchain.md",
  "updated_at": "2026-05-12T10:23:33.477Z"
}
```

## Introduction

This guide walks you through connecting Recruitee to LangChain using the Composio tool router. By the end, you'll have a working Recruitee agent that can add a new candidate named alex lee, list all currently published job offers, get detailed profile for candidate emily chen through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Recruitee account through Composio's Recruitee MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Recruitee with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Recruitee project to Composio
- Create a Tool Router MCP session for Recruitee
- Initialize an MCP client and retrieve Recruitee tools
- Build a LangChain agent that can interact with Recruitee
- 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 Recruitee MCP server, and what's possible with it?

The Recruitee MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Recruitee account. It provides structured and secure access to your recruitment workflow, so your agent can perform actions like managing candidates, creating notes, publishing job offers, retrieving company info, and handling tags on your behalf.
- Automated candidate management: Quickly create new candidate profiles, retrieve detailed information, or delete candidates as your hiring process evolves.
- Collaborative note-taking: Let your agent add notes to candidate profiles, ensuring every piece of feedback or interview insight is captured and accessible.
- Job offer publishing and retrieval: Effortlessly generate new job offers or fetch details on published positions from your public careers site.
- Company and job listing access: Instantly get your company ID, list all candidates, or pull a list of current published job offers for reporting and coordination.
- Tag and label management: Enable your agent to delete outdated tags, keeping your recruitment database organized and relevant.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RECRUITEE_CREATE_CANDIDATE` | Create Candidate | Tool to create a new candidate profile. Use after gathering all candidate details. Example: "Create a new candidate named Jane Doe with email jane.doe@example.com." |
| `RECRUITEE_CREATE_NOTE` | Create Note | Creates a new note for a candidate in Recruitee. Notes can be used to record interview feedback, assessments, or any observations about the candidate. Use this when you need to add commentary or documentation to a candidate's profile. |
| `RECRUITEE_CREATE_OFFER` | Create Offer | Creates a new job offer or talent pool in Recruitee. Required fields include title, location IDs, and description. Use Get Locations action to retrieve valid location IDs before creating an offer. The offer status can be set to draft, internal, published, closed, or archived. |
| `RECRUITEE_DELETE_CANDIDATE` | Delete Candidate | Tool to delete a candidate profile. Use when you need to permanently remove a candidate from your Recruitee account. Returns no content on success. |
| `RECRUITEE_DELETE_TAG` | Delete Tag | Permanently deletes a tag from Recruitee by its ID. This action requires appropriate API permissions to delete tags. Use this when you need to remove unused or obsolete tags. Note: Deleting a tag removes it from all associated candidates and offers. |
| `RECRUITEE_GET_CANDIDATE` | Get Candidate | Tool to retrieve detailed information about a specific candidate. Use when you need the candidate's full profile before proceeding. |
| `RECRUITEE_GET_CANDIDATES` | Get Candidates | Tool to retrieve a list of all candidates in the company. Use when you need to fetch or filter candidates before proceeding. |
| `RECRUITEE_GET_COMPANY_ID` | Get Company ID | Tool to retrieve the company ID of the authenticated account. Use when you need to confirm your company identity before other operations. |
| `RECRUITEE_GET_COMPANY_OFFER_PUBLIC` | Get Company Offer Public | Tool to retrieve a specific published job offer by ID or slug from the public Careers Site API. Use after you have the offer identifier. |
| `RECRUITEE_GET_DEPARTMENTS` | Get Departments | Tool to retrieve a list of company departments. Use when you need to reference or assign offers or candidates to departments. |
| `RECRUITEE_GET_LOCATIONS` | Get Locations | Tool to retrieve a list of company locations. Use when you need to see all location options before assigning them to offers. |
| `RECRUITEE_GET_NOTES` | Get Notes | Tool to retrieve a list of notes for a specific candidate. Use after confirming the candidate exists when you need to review their notes. |
| `RECRUITEE_GET_OFFERS` | Get Offers | Tool to retrieve a list of all job offers. Use after authentication to browse or paginate your company's complete set of offers. |
| `RECRUITEE_GET_PIPELINE_STAGES` | Get Pipeline Stages | Tool to retrieve pipeline stages of a job offer. Use when you have the offer ID and need its stages to track candidate progression. Example: "Get pipeline stages for offer ID 456." |
| `RECRUITEE_GET_TAGS` | Get Tags | Retrieve all tags with optional filtering and pagination. Search by name, sort by name or usage count, and paginate through results. |
| `RECRUITEE_LIST_EEO_JOB_CATEGORIES` | List EEO Job Categories | Tool to retrieve available EEO (Equal Employment Opportunity) job categories. Use when you need to see standard EEO job classification options. |
| `RECRUITEE_LIST_INVOICES` | List Invoices | Tool to list invoices for a company. Use to retrieve billing invoice records. |
| `RECRUITEE_LIST_LOCALIZATION_SETTINGS` | List Localization Settings | Tool to retrieve localization settings including proposed time format and start day of the week. Use when you need to check regional or time display preferences. |
| `RECRUITEE_LIST_SHARE_COUNTRIES` | List Share Countries | Tool to retrieve all countries with region codes and phone codes per locale. Use when you need comprehensive country reference data including internationalization details. |
| `RECRUITEE_LIST_SHARE_EEO_ANSWERS` | List Share EEO Answers | Tool to retrieve available EEO (Equal Employment Opportunity) answers. Use when you need to see available answer options for EEO compliance questions. |
| `RECRUITEE_UPDATE_CANDIDATE` | Update Candidate | Updates an existing candidate's information in Recruitee. Use this to modify candidate details such as name, contact info, cover letter, tags, and social links. All fields except candidate_id are optional - only provide the fields you want to update. The API performs a partial update (PATCH), preserving any fields you don't specify. |
| `RECRUITEE_UPDATE_NOTE` | Update Note | Tool to update an existing note for a candidate. Use when you need to modify note text or pin status after creation. |
| `RECRUITEE_UPDATE_OFFER` | Update Offer | Updates an existing job offer or talent pool in Recruitee. Allows modification of offer details including title, description, requirements, status, locations, department assignment, work type (remote/hybrid/on-site), visibility settings, and application form field requirements. Only specified fields are updated; omitted fields remain unchanged. Requires the offer ID - use Get Offers or Get Offer actions to retrieve existing offer IDs. |

## Supported Triggers

None listed.

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

The Recruitee MCP server is an implementation of the Model Context Protocol that connects your AI agent to Recruitee. It provides structured and secure access so your agent can perform Recruitee 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 Recruitee 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 Recruitee 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 Recruitee tools as needed
```python
# Create Tool Router session for Recruitee
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['recruitee']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "recruitee-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({
    "recruitee-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 Recruitee 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 Recruitee 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=['recruitee']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "recruitee-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 Recruitee 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: ['recruitee']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "recruitee-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 Recruitee 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 Recruitee 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 Recruitee MCP Agent with another framework

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

## Related Toolkits

- [Ashby](https://composio.dev/toolkits/ashby) - Ashby is an applicant tracking system that handles job postings, candidate management, and hiring analytics.
- [Async interview](https://composio.dev/toolkits/async_interview) - Async interview is an on-demand video interview platform for streamlined hiring. Candidates record responses on their schedule, so employers can review anytime.
- [Bamboohr](https://composio.dev/toolkits/bamboohr) - BambooHR is a cloud-based HR management platform for small and mid-sized businesses. It streamlines employee data, HR workflows, and reporting in one easy interface.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Connecteam](https://composio.dev/toolkits/connecteam) - Connecteam is a workforce management platform for deskless teams, streamlining operations, HR, and team communication. It helps businesses save time by automating scheduling, time tracking, and staff engagement tasks.
- [Lever](https://composio.dev/toolkits/lever) - Lever is an applicant tracking system that blends sourcing, CRM, and analytics for recruiting. It helps companies scale hiring with collaborative workflows and actionable insights.
- [Remote retrieval](https://composio.dev/toolkits/remote_retrieval) - Remote retrieval is a logistics automation tool for managing laptop and monitor returns. It streamlines return tracking, saving time and hassle for IT and ops teams.
- [Sap successfactors](https://composio.dev/toolkits/sap_successfactors) - Sap successfactors is a cloud-based human capital management suite for HR, payroll, recruiting, and talent management. It helps organizations centralize employee data and streamline the entire employee lifecycle.
- [Talenthr](https://composio.dev/toolkits/talenthr) - TalentHR is an intuitive, all-in-one HR tool for managing employee records, leave, and HR workflows. It streamlines HR operations so businesses can focus on people, not paperwork.
- [Workable](https://composio.dev/toolkits/workable) - Workable is an all-in-one HR software platform that streamlines hiring, employee management, and payroll. It helps teams simplify recruiting, onboarding, and staff operations in one place.
- [Workday](https://composio.dev/toolkits/workday) - Workday is a cloud-based ERP platform for HR, finance, and workforce analytics. It streamlines employee management, payroll, and business operations in a single system.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide.
- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

## Frequently Asked Questions

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

With a standalone Recruitee MCP server, the agents and LLMs can only access a fixed set of Recruitee tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Recruitee 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 Recruitee tools.

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

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

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