# How to integrate Bamboohr MCP with LlamaIndex ```json { "title": "How to integrate Bamboohr MCP with LlamaIndex", "toolkit": "Bamboohr", "toolkit_slug": "bamboohr", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/bamboohr/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/bamboohr/framework/llama-index.md", "updated_at": "2026-05-12T10:02:22.574Z" } ``` ## Introduction This guide walks you through connecting Bamboohr to LlamaIndex using the Composio tool router. By the end, you'll have a working Bamboohr agent that can add new dependent for employee john doe, update direct deposit details for sarah smith, log overtime hours for marketing team members through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Bamboohr account through Composio's Bamboohr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Bamboohr with - [OpenAI Agents SDK](https://composio.dev/toolkits/bamboohr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/bamboohr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/bamboohr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/bamboohr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/bamboohr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/bamboohr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/bamboohr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/bamboohr/framework/cli) - [Google ADK](https://composio.dev/toolkits/bamboohr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/bamboohr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/bamboohr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/bamboohr/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/bamboohr/framework/crew-ai) ## TL;DR Here's what you'll learn: - Set your OpenAI and Composio API keys - Install LlamaIndex and Composio packages - Create a Composio Tool Router session for Bamboohr - Connect LlamaIndex to the Bamboohr MCP server - Build a Bamboohr-powered agent using LlamaIndex - Interact with Bamboohr through natural language ## What is LlamaIndex? LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools. Key features include: - ReAct Agent: Reasoning and acting pattern for tool-using agents - MCP Tools: Native support for Model Context Protocol - Context Management: Maintain conversation context across interactions - Async Support: Built for async/await patterns ## What is the Bamboohr MCP server, and what's possible with it? The BambooHR MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your BambooHR account. It provides structured and secure access to your HR data, so your agent can perform actions like managing employee benefits, updating payroll records, tracking time, and assisting with applicant management on your behalf. - Employee benefits administration: Automatically enroll employees in benefit groups, create or update benefit records, and manage company-wide benefit offerings with ease. - Payroll and direct deposit management: Enable your agent to create paystubs, add unpaid pay periods, and update employee direct deposit information for seamless payroll processing. - Dependent and tax record updates: Empower your agent to add employee dependents and modify withholding details, keeping employee records accurate and compliant. - Time tracking automation: Let your agent log new time tracking records for employees, ensuring precise attendance and overtime data for reporting and payroll. - Applicant and recruitment collaboration: Allow your agent to post comments on applicant records, streamlining feedback and communication during the hiring process. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `BAMBOOHR_ATS_CREATE_CANDIDATE` | Create Candidate Application | Tool to create a candidate application. Use when adding a new applicant to a specific job opening in BambooHR ATS. | | `BAMBOOHR_ATS_CREATE_JOB_OPENING` | Create Job Opening | Tool to create a new job opening in BambooHR ATS. Use when you need to open a new job for applicants. | | `BAMBOOHR_ATS_GET_APPLICATIONS` | List Job Applications | Tool to list job applications with optional filters. Use when retrieving ATS applications for reporting or integration. | | `BAMBOOHR_BENEFIT_GET_COVERAGES` | Get Benefit Coverages | Tool to retrieve standard benefit coverage options. Use when you need to fetch all or specific coverages before configuring benefit plans. | | `BAMBOOHR_BENEFIT_GET_MEMBER_EVENTS` | Get Member Benefit Events | Tool to list member benefit events. Use when you need to fetch all benefit events visible to the authenticated user. | | `BAMBOOHR_COMPANY_GET_EINS` | Get Company EINs | Tool to retrieve company Employer Identification Numbers (EINs). Use when you need to fetch your account's EINs after authenticating. | | `BAMBOOHR_COMPANY_GET_INFORMATION` | Get Company Information | Tool to retrieve company information. Use when you need details about the current account's settings. | | `BAMBOOHR_CREATE_FILE_CATEGORY` | Create File Category | Tool to create new company file categories. Use when organizing company files by adding new categories after confirming desired names. | | `BAMBOOHR_CREATE_TIME_OFF_REQUEST` | Create Time Off Request | Tool to submit a new time off request. Use after confirming employee ID and valid payload. | | `BAMBOOHR_DATASETS_LIST` | List Datasets | Tool to list available datasets via the Datasets API. Use when you need to discover available dataset schemas before querying data. | | `BAMBOOHR_DEPENDENTS_CREATE` | Create Employee Dependent | Tool to add a dependent to an employee. Use when a new dependent needs to be recorded for benefits or HR tracking. Ensure the employee record exists before calling this tool. | | `BAMBOOHR_DEPENDENTS_GET_ALL` | Get All Employee Dependents | Tool to retrieve all employee dependents. Use after ensuring your API key has access to benefit settings. | | `BAMBOOHR_EMPLOYEE_CREATE` | Create Employee | Tool to create a new employee record. Use when adding employees via BambooHR API. Returns the new employee's ID. | | `BAMBOOHR_EMPLOYEE_FILES_CREATE_CATEGORY` | Create Employee File Category | Tool to create new employee file categories. Use when organizing employee files by adding new categories after confirming desired names. | | `BAMBOOHR_EMPLOYEE_GET_CHANGED` | Get Changed Employees | Tool to get employees inserted, updated, or deleted since a given timestamp. Use when fetching incremental HR changes. | | `BAMBOOHR_FILES_LIST` | List Company Files | Tool to list company file categories and their files. Use when you need to retrieve all company files organized by category after confirming file inventory exists. | | `BAMBOOHR_FILES_UPLOAD` | Upload Company File | Tool to upload a new company file. Use when you need to add a file to BambooHR after confirming its category and share settings. | | `BAMBOOHR_GET_ALL_EMPLOYEES` | Get All Employees | Retrieves all employees from the BambooHR employee directory including their basic information and status. | | `BAMBOOHR_GET_APPLICANT_STATUSES` | Get Applicant Statuses | Tool to retrieve applicant statuses. Use when you need to list ATS statuses for your company; requires ATS settings access. | | `BAMBOOHR_GET_CUSTOM_EMPLOYEE_FIELDS` | Get Custom Employee Fields | Tool to fetch custom employee field values. Use when you need to retrieve only custom fields for all employees. | | `BAMBOOHR_GET_CUSTOM_REPORTS` | Run Custom Report | Tool to run a custom report by ID or ad-hoc fields. Use when you need to execute a saved report or generate an ad-hoc report and retrieve its results in JSON or file format. | | `BAMBOOHR_GET_EMPLOYEE` | Get Employee | Tool to retrieve detailed information for a specific employee. Use when you need individual employee data by ID. | | `BAMBOOHR_GET_EMPLOYEE_PHOTO` | Get Employee Photo | Tool to retrieve an employee's profile photo by size. Use when you need to download the image binary for the specified employee after confirming their ID. | | `BAMBOOHR_GET_HIRING_LEADS` | Get Hiring Leads | Tool to retrieve potential hiring leads (employees who can manage job openings) for use in creating a new job opening. The API key owner must have access to ATS settings. | | `BAMBOOHR_GET_JOB_SUMMARIES` | Get Job Summaries | Tool to retrieve a list of ATS job summaries. Use when you need an overview of all job postings and their key details. | | `BAMBOOHR_GET_META_DEPARTMENTS` | Get Departments Metadata | Tool to list department metadata. Use after needing all available department codes and names. | | `BAMBOOHR_GET_META_DIVISIONS` | Get Meta Divisions | Tool to list all division metadata. Use after authenticating to fetch the account's divisions. | | `BAMBOOHR_GET_META_EMPLOYMENT_STATUSES` | List Employment Status Metadata | Tool to list all employment status metadata. Use when you need all defined employment statuses for the company. | | `BAMBOOHR_GET_META_JOB_TITLES` | Get Meta Job Titles | Tool to retrieve job title metadata. Uses the list-field metadata endpoint and extracts the options for the `jobTitle` field. | | `BAMBOOHR_GET_META_LOCATIONS` | Get Meta Locations | Tool to list location metadata. Use when you need all configured company locations for lookups. | | `BAMBOOHR_GET_META_TIME_OFF_TYPES` | Get Time-Off Types Metadata | Tool to list time-off type metadata. Use when you need to discover available time-off types before creating time-off requests. | | `BAMBOOHR_GET_REPORT` | Get Report | Tool to fetch a built-in or published report in JSON or other formats. Use when you need to retrieve report data or download report files after specifying the report ID and desired output format. | | `BAMBOOHR_GET_TIME_OFF_BALANCES` | Get Time-Off Balances | Tool to retrieve time-off balances for employees. Use when you need current balances across your team. | | `BAMBOOHR_GET_TIME_OFF_REQUESTS` | Get Time-Off Requests | Tool to list time-off requests within a date range. Use after confirming start and end dates; supports optional filters for status, employee, and time off type. | | `BAMBOOHR_LIST_BUILTIN_REPORTS` | List Company Reports | Tool to list all available company and custom reports. Use after confirming account setup. Requires 'report' scope for OAuth or an API key with report access permissions. | | `BAMBOOHR_META_GET_COUNTRIES` | Get Country Options | Tool to retrieve all available country options. Use when you need a complete list of selectable countries before updating or validating country fields. | | `BAMBOOHR_META_GET_LIST_FIELD_DETAILS` | Get List Field Details | Tool to get details for all list fields. Use when you need to discover list field options before using them. | | `BAMBOOHR_META_GET_TABULAR_FIELDS` | Get Tabular Fields Metadata | Tool to list tabular table fields metadata. Use when you need standard table structures before accessing table rows. | | `BAMBOOHR_META_GET_USERS` | Get Users | Tool to list active users with basic info. Use when you need to retrieve current users' IDs, names, and emails. | | `BAMBOOHR_UPDATE_EMPLOYEE` | Update Employee | Tool to update fields on a specified employee record. Use when you need to modify employee properties via BambooHR API after confirming the target employee ID. Example: "Update employee 12345's department to Sales". | | `BAMBOOHR_UPDATE_TIME_OFF_REQUEST` | Update Time Off Request | Tool to update the status of an existing time-off request. Use when you need to approve, deny, or cancel a request after reviewing it. Example: "Approve time-off request 12345". | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Bamboohr MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bamboohr. It provides structured and secure access so your agent can perform Bamboohr operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before you begin, make sure you have: - Python 3.8/Node 16 or higher installed - A Composio account with the API key - An OpenAI API key - A Bamboohr account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Bamboohr No description provided. ### 2. Installing dependencies No description provided. ```python pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv ``` ```typescript npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv ``` ### 3. Set environment variables Create a .env file in your project root: These credentials will be used to: - Authenticate with OpenAI's GPT-5 model - Connect to Composio's Tool Router - Identify your Composio user session for Bamboohr access ```bash OPENAI_API_KEY=your-openai-api-key COMPOSIO_API_KEY=your-composio-api-key COMPOSIO_USER_ID=your-user-id ``` ### 4. Import modules No description provided. ```python import asyncio import os import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); ``` ### 5. Load environment variables and initialize Composio No description provided. ```python OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set in the environment") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment") ``` ```typescript const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set"); if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set"); ``` ### 6. Create a Tool Router session and build the agent function What's happening here: - We create a Composio client using your API key and configure it with the LlamaIndex provider - We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, bamboohr) - The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools - LlamaIndex will connect to this endpoint to dynamically discover and use the available Bamboohr tools. - The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent. ```python async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["bamboohr"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Bamboohr actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Bamboohr actions. """ return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True) ``` ```typescript async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["bamboohr"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description : "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Bamboohr actions." , llm, tools, }); return agent; } ``` ### 7. Create an interactive chat loop No description provided. ```python async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") ``` ```typescript async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } ``` ### 8. Define the main entry point What's happening here: - We're orchestrating the entire application flow - The agent gets built with proper error handling - Then we kick off the interactive chat loop so you can start talking to Bamboohr ```python async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err) { console.error("Failed to start agent:", err); process.exit(1); } } main(); ``` ### 9. Run the agent When prompted, authenticate and authorise your agent with Bamboohr, then start asking questions. ```bash python llamaindex_agent.py ``` ```typescript npx ts-node llamaindex-agent.ts ``` ## Complete Code ```python import asyncio import os import signal import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["bamboohr"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Bamboohr actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Bamboohr actions. """ return ReActAgent( tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True, ); async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { LlamaindexProvider } from "@composio/llamaindex"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) { throw new Error("OPENAI_API_KEY is not set in the environment"); } if (!COMPOSIO_API_KEY) { throw new Error("COMPOSIO_API_KEY is not set in the environment"); } if (!COMPOSIO_USER_ID) { throw new Error("COMPOSIO_USER_ID is not set in the environment"); } async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["bamboohr"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description: "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Bamboohr actions." , llm, tools, }); return agent; } async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err: any) { console.error("Failed to start agent:", err?.message ?? err); process.exit(1); } } main(); ``` ## Conclusion You've successfully connected Bamboohr to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Bamboohr tools through an MCP endpoint - LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations - The agent becomes more capable without increasing prompt size - Async Python provides clean, efficient execution of agent workflows You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter. ## How to build Bamboohr MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/bamboohr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/bamboohr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/bamboohr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/bamboohr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/bamboohr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/bamboohr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/bamboohr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/bamboohr/framework/cli) - [Google ADK](https://composio.dev/toolkits/bamboohr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/bamboohr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/bamboohr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/bamboohr/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/bamboohr/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. - [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. - [Recruitee](https://composio.dev/toolkits/recruitee) - Recruitee is collaborative hiring software that centralizes recruitment tasks for teams. It streamlines sourcing, interviewing, and hiring so you can fill roles faster. - [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 Bamboohr MCP? With a standalone Bamboohr MCP server, the agents and LLMs can only access a fixed set of Bamboohr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Bamboohr and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with LlamaIndex? Yes, you can. LlamaIndex 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 Bamboohr tools. ### Can I manage the permissions and scopes for Bamboohr while using Tool Router? Yes, absolutely. You can configure which Bamboohr 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 Bamboohr data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)