How to integrate Jobnimbus MCP with LangChain

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.

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JobNimbus is a CRM and project management platform built for contractors. It streamlines scheduling, estimates, invoicing, and job tracking to simplify your workflow.

21 Tools

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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

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.

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

What is Composio SDK?

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

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

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

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

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

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
5

Import dependencies

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();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Jobnimbus functionality through MCP
6

Initialize Composio client

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()
    });
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
7

Create a Tool Router session

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

const url = session.mcp.url;
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
8

Configure the agent with the 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 });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Jobnimbus MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Jobnimbus tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

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);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Jobnimbus and LangChain:

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.
TOOLS

Supported Tools

Every Jobnimbus action and event your agent gets out of the box.

Create Location

Tool to create a new location in JobNimbus.

Get Account Settings

Tool to retrieve account-wide settings (workflows, types, sources).

Get Activity by ID

Retrieves a specific JobNimbus activity by its unique jnid.

Get Contact by ID

Tool to retrieve a contact by ID.

List Contacts

Tool to list all contacts.

Update Contact

Tool to update an existing contact.

Create File Attachment Type

Creates a new file attachment type in JobNimbus.

Create Material Order

Creates a new material order in JobNimbus.

Create Task

Tool to create a new task.

Create Workflow Status

Tool to create a new status in an existing workflow.

Get File Attachment Content by ID

Retrieves the raw content of a specific file attachment from JobNimbus by its unique ID.

List Activities

Tool to retrieve all activities.

List Invoices

Tool to list all invoices (v2).

List Material Orders

Tool to list all material orders (v2).

List Payments

Tool to retrieve payments list with optional filters.

List Products

Tool to list all products.

List Work Orders

Tool to retrieve all work orders (v2).

Get Product by ID

Retrieves detailed information about a specific JobNimbus product using its jnid.

List Tasks

Tool to list all tasks.

Update Task

Update an existing JobNimbus task by its jnid.

Get Units of Measure

Tool to retrieve list of supported units of measure.

FRAMEWORKS

How to build Jobnimbus MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Jobnimbus MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Jobnimbus MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Jobnimbus MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Jobnimbus MCP with Claude Cowork

Codex logo

Codex

Use Jobnimbus MCP with Codex

OpenClaw logo

OpenClaw

Use Jobnimbus MCP with OpenClaw

Hermes logo

Hermes

Use Jobnimbus MCP with Hermes

CLI logo

CLI

Use Jobnimbus MCP with CLI

Google ADK logo

Google ADK

Use Jobnimbus MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Jobnimbus MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Jobnimbus MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Jobnimbus MCP with LlamaIndex

CrewAI logo

CrewAI

Use Jobnimbus MCP with CrewAI

FAQ

Frequently asked questions

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.

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.

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.

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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