# How to integrate Jobnimbus MCP with LangChain

```json
{
  "title": "How to integrate Jobnimbus MCP with LangChain",
  "toolkit": "Jobnimbus",
  "toolkit_slug": "jobnimbus",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/jobnimbus/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/jobnimbus/framework/langchain.md",
  "updated_at": "2026-05-12T10:16:29.914Z"
}
```

## Introduction

This guide walks you through connecting Jobnimbus to LangChain using the Composio tool router. By the end, you'll have a working Jobnimbus agent that can list all open tasks for this week, create a new material order for a contact, fetch details for contact by name through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Jobnimbus account through Composio's Jobnimbus MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Jobnimbus with

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

## TL;DR

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

The Jobnimbus MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jobnimbus account. It provides structured and secure access to your CRM and project management data, so your agent can perform actions like managing contacts, scheduling tasks, creating locations, and retrieving account information on your behalf.
- Contact management and lookup: Instantly create new contacts or fetch full details and lists of existing contacts for streamlined project tracking and reporting.
- Task scheduling and tracking: Direct your agent to create and assign tasks, helping teams stay organized and on top of project to-dos.
- Location and job site creation: Quickly add new locations to your Jobnimbus account, ensuring every job and address is properly logged for future reference.
- Material order and workflow automation: Let your agent place material orders for jobs and update workflow statuses to keep projects moving smoothly from lead to completion.
- Account and attachment management: Retrieve account settings or pull file attachments by ID, supporting seamless document handling and system configuration.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `JOBNIMBUS_ACCOUNT_CREATE_LOCATION` | Create Location | Tool to create a new location in JobNimbus. Use after gathering address and contact details. |
| `JOBNIMBUS_ACCOUNT_GET_SETTINGS` | Get Account Settings | Tool to retrieve account-wide settings (workflows, types, sources). Use after authenticating to load or refresh workflow and source mappings. |
| `JOBNIMBUS_ACTIVITY_GET` | Get Activity by ID | Retrieves a specific JobNimbus activity by its unique jnid. Activities in JobNimbus represent logged events such as task modifications, contact updates, job creation, and other system actions. Each activity contains details about what changed, who made the change, and when it occurred. Use this action when you need detailed information about a specific activity, such as viewing the full history of changes or understanding who performed an action. |
| `JOBNIMBUS_CONTACT_GET` | Get Contact by ID | Tool to retrieve a contact by ID. Use after obtaining the contact’s jnid to fetch full details. |
| `JOBNIMBUS_CONTACT_LIST` | List Contacts | Tool to list all contacts. Use when you need to fetch multiple contacts, e.g., for reporting or synchronization. |
| `JOBNIMBUS_CONTACT_UPDATE` | Update Contact | Tool to update an existing contact. Use when you have a contact's JNID and need to modify its fields. Call after fetching or creating a contact. |
| `JOBNIMBUS_CREATE_FILE_TYPE` | Create File Attachment Type | Creates a new file attachment type in JobNimbus. File types are custom categories used to organize and classify document attachments (e.g., contracts, warranties, photos, permits). You must create a file type before you can upload files with that category. |
| `JOBNIMBUS_CREATE_MATERIAL_ORDER` | Create Material Order | Creates a new material order in JobNimbus. A material order tracks materials needed for a job and can be submitted to suppliers. Material orders must be linked to a contact or job record and include at least one line item referencing an existing product from your Products & Services catalog. Prerequisites: - At least one contact or job record must exist (use JOBNIMBUS_CONTACT_LIST to find contacts) - Products must exist in your catalog (use JOBNIMBUS_LIST_PRODUCTS to find product IDs) Note: Custom line items are not supported - all items must reference existing products by their jnid. |
| `JOBNIMBUS_CREATE_TASK` | Create Task | Tool to create a new task. Use when scheduling or tracking tasks linked to contacts or jobs. |
| `JOBNIMBUS_CREATE_WORKFLOW_STATUS` | Create Workflow Status | Tool to create a new status in an existing workflow. Use after confirming the workflow ID to add specialized status entries like 'Lead' or 'Inspection'. |
| `JOBNIMBUS_FILE_GET` | Get File Attachment Content by ID | Retrieves the raw content of a specific file attachment from JobNimbus by its unique ID. This action downloads the actual file content (binary data for PDFs, images, etc.) but does NOT return file metadata like filename, content type, or size. If you need metadata, use the files list endpoint instead. Common use case: Download a file attachment after obtaining its jnid from a list files query or from a related record (contact, job, etc.). |
| `JOBNIMBUS_LIST_ACTIVITIES` | List Activities | Tool to retrieve all activities. Use after authentication to fetch a paginated list of activities. |
| `JOBNIMBUS_LIST_INVOICES` | List Invoices | Tool to list all invoices (v2). Use when you need to fetch multiple invoice records. |
| `JOBNIMBUS_LIST_MATERIAL_ORDERS` | List Material Orders | Tool to list all material orders (v2). Use after authentication to fetch multiple material order records. |
| `JOBNIMBUS_LIST_PAYMENTS` | List Payments | Tool to retrieve payments list with optional filters. Use after auth. |
| `JOBNIMBUS_LIST_PRODUCTS` | List Products | Tool to list all products. Use after authentication to fetch full product catalog. |
| `JOBNIMBUS_LIST_WORKORDERS` | List Work Orders | Tool to retrieve all work orders (v2). Use after authentication when you need a paginated list of work orders. |
| `JOBNIMBUS_PRODUCT_GET` | Get Product by ID | Retrieves detailed information about a specific JobNimbus product using its jnid. Use this action when you need to get full details about a product, including pricing, cost, unit of measurement, and tax settings. Obtain the jnid first using the List Products action. |
| `JOBNIMBUS_TASK_LIST` | List Tasks | Tool to list all tasks. Use when you need an overview of tasks for planning or review. |
| `JOBNIMBUS_UPDATE_TASK` | Update Task | Update an existing JobNimbus task by its jnid. Allows updating task details like title, description, dates, priority, and task type. Use List Tasks action to find task jnids. Note: To update task type, both record_type and record_type_name must be provided together. |
| `JOBNIMBUS_UTILITY_GET_UOMS` | Get Units of Measure | Tool to retrieve list of supported units of measure. Use after authenticating when you need to present or validate measurement units. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

- [Hubspot](https://composio.dev/toolkits/hubspot) - HubSpot is an all-in-one marketing, sales, and customer service platform. It lets teams nurture leads, automate outreach, and track every customer interaction in one place.
- [Pipedrive](https://composio.dev/toolkits/pipedrive) - Pipedrive is a sales management platform offering pipeline visualization, lead tracking, and workflow automation. It helps sales teams keep deals moving forward efficiently and never miss a follow-up.
- [Salesforce](https://composio.dev/toolkits/salesforce) - Salesforce is a leading CRM platform that helps businesses manage sales, service, and marketing. It centralizes customer data, enabling teams to drive growth and build strong relationships.
- [Apollo](https://composio.dev/toolkits/apollo) - Apollo is a CRM and lead generation platform that helps businesses discover contacts and manage sales pipelines. Use it to streamline customer outreach and track your deals from one place.
- [Attio](https://composio.dev/toolkits/attio) - Attio is a customizable CRM and workspace for managing your team's relationships and workflows. It helps teams organize contacts, automate tasks, and collaborate more efficiently.
- [Acculynx](https://composio.dev/toolkits/acculynx) - AccuLynx is a cloud-based roofing business management software for contractors. It streamlines project tracking, lead management, and document sharing.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Affinity](https://composio.dev/toolkits/affinity) - Affinity is a relationship intelligence CRM that helps private capital investors find, manage, and close more deals. It streamlines deal flow and surfaces key connections to help you win opportunities.
- [Agencyzoom](https://composio.dev/toolkits/agencyzoom) - AgencyZoom is a sales and performance platform built for P&C insurance agencies. It helps agents boost sales, retain clients, and analyze producer results in one place.
- [Bettercontact](https://composio.dev/toolkits/bettercontact) - Bettercontact is a smart contact enrichment tool for finding emails and phone numbers. It helps boost lead generation with automated, waterfall search across multiple sources.
- [Blackbaud](https://composio.dev/toolkits/blackbaud) - Blackbaud provides cloud-based software for nonprofits, schools, and healthcare institutions. It streamlines fundraising, donor management, and mission-driven operations.
- [Brilliant directories](https://composio.dev/toolkits/brilliant_directories) - Brilliant Directories is an all-in-one platform for building and managing online membership communities and business directories. It streamlines listings, member management, and engagement tools into a single, easy interface.
- [Capsule crm](https://composio.dev/toolkits/capsule_crm) - Capsule CRM is a user-friendly CRM platform for managing contacts and sales pipelines. It helps businesses organize relationships and streamline their sales process efficiently.
- [Centralstationcrm](https://composio.dev/toolkits/centralstationcrm) - CentralStationCRM is an easy-to-use CRM software focused on collaboration and long-term customer relationships. It helps teams manage contacts, deals, and communications all in one place.
- [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.
- [Close](https://composio.dev/toolkits/close) - Close is a CRM platform built for sales teams, combining calling, email automation, and predictive dialers. It streamlines sales workflows and boosts productivity with all-in-one communication tools.
- [Dropcontact](https://composio.dev/toolkits/dropcontact) - Dropcontact is a B2B email finder and data enrichment service for professionals. It delivers verified email addresses and enriches contact info with up-to-date data.
- [Dynamics365](https://composio.dev/toolkits/dynamics365) - Dynamics 365 is Microsoft's platform combining CRM, ERP, and productivity apps. It streamlines sales, marketing, service, and operations in one place.
- [Espocrm](https://composio.dev/toolkits/espocrm) - EspoCRM is an open-source web application for managing customer relationships. It helps businesses organize contacts, track leads, and streamline their sales process.
- [Fireberry](https://composio.dev/toolkits/fireberry) - Fireberry is a CRM platform that streamlines customer and sales management. It helps businesses organize contacts, automate sales, and integrate with other business tools.

## Frequently Asked Questions

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

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

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

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

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