# How to integrate Acculynx MCP with LlamaIndex

```json
{
  "title": "How to integrate Acculynx MCP with LlamaIndex",
  "toolkit": "Acculynx",
  "toolkit_slug": "acculynx",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/acculynx/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/acculynx/framework/llama-index.md",
  "updated_at": "2026-05-06T07:59:10.340Z"
}
```

## Introduction

This guide walks you through connecting Acculynx to LlamaIndex using the Composio tool router. By the end, you'll have a working Acculynx agent that can add new roofing lead from web form, schedule site visit for job tomorrow, list all appointments for job 12345 through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Acculynx account through Composio's Acculynx MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Acculynx with

- [OpenAI Agents SDK](https://composio.dev/toolkits/acculynx/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/acculynx/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/acculynx/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/acculynx/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/acculynx/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/acculynx/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/acculynx/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/acculynx/framework/cli)
- [Google ADK](https://composio.dev/toolkits/acculynx/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/acculynx/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/acculynx/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/acculynx/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/acculynx/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 Acculynx
- Connect LlamaIndex to the Acculynx MCP server
- Build a Acculynx-powered agent using LlamaIndex
- Interact with Acculynx 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 Acculynx MCP server, and what's possible with it?

The Acculynx MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Acculynx account. It provides structured and secure access to your construction project data, so your agent can create jobs, manage contacts, schedule appointments, and organize calendars on your behalf.
- Automated job creation and management: Instantly create new jobs in your Acculynx system, specifying contacts, addresses, categories, and more to streamline project setup.
- Contact and lead management: Add new contacts or leads with detailed information, helping you keep your pipeline up to date and organized without manual data entry.
- Appointment scheduling and tracking: Schedule initial job appointments or retrieve summaries of all job-related events, making it easy to keep teams and clients in sync.
- Company representative assignment: Assign representatives to specific jobs to clarify project responsibilities and maintain accurate records of team involvement.
- Calendar and contact type retrieval: Fetch lists of company calendars or contact types, supporting smarter scheduling, filtering, and contact management across your organization.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ACCULYNX_ADD_JOB_APPOINTMENT` | Add job appointment | This endpoint allows users to schedule the initial appointment for a specific job in the acculynx system. it is used to set up the first meeting or site visit for a construction or roofing project. the endpoint requires the job id, start date and time, and end date and time for the appointment. this is crucial for initiating the project workflow and ensuring that all parties involved are aware of the scheduled time for the first interaction. the appointment details are set in the context of the company's timezone unless otherwise specified. use this endpoint when a new job has been created and the first appointment needs to be scheduled with the client or at the job site. |
| `ACCULYNX_CREATE_A_CONTACT` | Create a contact | Creates a new contact in the acculynx system with detailed information for use in roofing and construction project management. this endpoint allows for the addition of comprehensive contact details including personal information, company affiliation, communication preferences, and address information. it's particularly useful for adding new customers, leads, vendors, or any other type of contact relevant to construction projects. the endpoint provides flexibility in the amount of information that can be added, with only the contact type being required. use this when you need to add a new contact to your acculynx database or update your system with new lead information. note that while many fields are optional, providing as much information as possible will enhance the usefulness of the contact record for future project management and communication purposes. |
| `ACCULYNX_CREATE_A_JOB` | Create a job | Creates a new job in the acculynx system with the provided details. this endpoint allows you to initialize a job with essential information such as the associated contact, location, job category, work type, priority, and trade types. it's particularly useful for setting up new projects or tasks within the acculynx platform for the roofing and construction industries. the endpoint requires at minimum a contact id and location address, with several optional fields to further customize the job entry. use this when you need to programmatically create new jobs in acculynx, such as when integrating with other systems or automating job creation processes. |
| `ACCULYNX_CREATE_A_LEAD` | Create a lead | This endpoint creates a new lead in the acculynx system, specifically for residential roofing projects. it should be used when a new potential customer expresses interest in roofing services or when importing lead data from external sources. the endpoint captures essential contact information to initiate the lead management process. while it creates the lead, it does not assign priorities or sales representatives; these actions would need to be performed separately. the endpoint is designed for simplicity and quick lead entry, focusing on the most crucial identifying information. |
| `ACCULYNX_JOB_APPOINTMENT_SUMMARY` | Job appointment summary | Retrieves a list of appointments from the calendar associated with a specific job in acculynx. this endpoint is used to fetch scheduled events, such as site visits, inspections, or project milestones, for a particular roofing or construction job. it provides valuable information for project management and scheduling purposes. the endpoint should be used when you need to view or manage the timeline of events for a specific job. it will not provide general calendar information or appointments unrelated to the specified job id. the response likely includes details such as appointment dates, times, descriptions, and associated team members, though the exact structure is not specified in the given schema. |
| `ACCULYNX_LIST_OF_CALENDARS_FOR_THE_LOCATION` | List of calendars for the location | Retrieves a list of calendars associated with the authenticated user or organization in acculynx. this endpoint provides access to the calendar data, which is crucial for scheduling and organizing tasks in the roofing and construction project management context. it should be used when you need to obtain an overview of all available calendars or to gather calendar ids for use in other api operations. the endpoint returns basic information about each calendar, likely including identifiers, names, and possibly associated metadata. it does not modify any calendar data and is intended for read-only operations. keep in mind that the response may be paginated for large datasets, and additional parameters might be available for filtering or sorting the results, although they are not specified in the current schema. |
| `ACCULYNX_LIST_OF_CONTACT_TYPES_RELATED_TO_THE_COMPANY` | List of contact types related to the company | Retrieves a list of all available contact types in the acculynx system. this endpoint is used to fetch the predefined categories or classifications for contacts, such as residential, repair, property management, and other job categories. it's essential for organizing and filtering contact information within the acculynx platform. the endpoint should be used when setting up new contacts, updating existing ones, or when needing to populate dropdown menus or filter options in the user interface. it does not create, modify, or delete contact types; it only provides the current list of available options. the response will likely include unique identifiers and names for each contact type, allowing for easy integration with other parts of the acculynx api or external systems. |
| `ACCULYNX_UPDATE_COMPANY_REPRESENTATIVE` | Update company representative | This endpoint allows you to add a company representative to a specific job within the acculynx system. it is used when you need to associate a representative with a particular project or task. the endpoint requires the job's unique identifier and the representative's id to establish the connection. this operation is useful for assigning personnel to projects, tracking responsibilities, and maintaining accurate job records. it's important to note that this endpoint only adds the association and doesn't create new representative or job entries. |

## Supported Triggers

None listed.

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

The Acculynx MCP server is an implementation of the Model Context Protocol that connects your AI agent to Acculynx. It provides structured and secure access so your agent can perform Acculynx 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 Acculynx account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Acculynx

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 Acculynx 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, acculynx)
- 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 Acculynx 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=["acculynx"],
    )

    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 Acculynx actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Acculynx 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: ["acculynx"],
    },
  );

  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 Acculynx 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<typeof createAgent>) {
  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 Acculynx
```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 Acculynx, 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=["acculynx"],
    )

    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 Acculynx actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Acculynx 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: ["acculynx"],
    },
  );

  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 Acculynx actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  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 Acculynx to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Acculynx 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 Acculynx MCP Agent with another framework

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

## Related Toolkits

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- [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.
- [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.
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- [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.
- [Firmao](https://composio.dev/toolkits/firmao) - Firmao is a business information platform offering company, industry, and market data. Use it to quickly research firms and gain competitive market insights.

## Frequently Asked Questions

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

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

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

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

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