# How to integrate Interzoid MCP with LlamaIndex

```json
{
  "title": "How to integrate Interzoid MCP with LlamaIndex",
  "toolkit": "Interzoid",
  "toolkit_slug": "interzoid",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/interzoid/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/interzoid/framework/llama-index.md",
  "updated_at": "2026-05-12T10:16:08.678Z"
}
```

## Introduction

This guide walks you through connecting Interzoid to LlamaIndex using the Composio tool router. By the end, you'll have a working Interzoid agent that can match duplicate customer records by name, verify email addresses in a contact list, enrich company data with industry details through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Interzoid account through Composio's Interzoid MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Interzoid with

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

The Interzoid MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Interzoid account. It provides structured and secure access to Interzoid's powerful data quality APIs, so your agent can perform actions like matching records, verifying data, enriching information, and analyzing datasets on your behalf.
- Data matching and deduplication: Let your agent detect and merge duplicate records across datasets using fuzzy and advanced matching algorithms.
- Real-time data verification: Have the agent verify email addresses, phone numbers, and other key data points to ensure accuracy and reliability.
- Data enrichment and augmentation: Automatically enhance your records with additional company, contact, or geographic information pulled from Interzoid's enrichment APIs.
- Similarity scoring and analysis: Enable your agent to compare names, addresses, or other fields for similarity, helping with record linkage or fraud detection.
- Automated quality checks: Easily set up workflows where your agent scans new or existing data for quality issues and suggests corrections or improvements.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `INTERZOID_ADDRESS_PARSE` | Parse Address | Tool to parse a free-form address into structured components. Use when you need to extract street, city, state, etc. from unstructured address strings. |
| `INTERZOID_EMAIL_TRUST_SCORE` | Interzoid Email Trust Score | Tool to return a trust score for an email address. Use when you need to assess the quality and legitimacy of an email address. Call after acquiring the target email. |
| `INTERZOID_GET_ADDRESS_MATCH_ADVANCED` | Get Address Match Advanced | Tool to generate a similarity key for a US street address. Use when performing fuzzy deduplication of addresses across datasets. |
| `INTERZOID_GET_AREA_CODE` | Get Area Code Information | Tool to retrieve telephone area code information including primary city and geographic locale. Use when you need to get details about a specific area code. |
| `INTERZOID_GET_AREA_CODE_FROM_NUMBER` | Get Area Code From Number | Tool to get area code information from a telephone number. Use when you need to identify the geographic location or area code details for a given phone number. |
| `INTERZOID_GET_BUSINESS_INFO` | Get Business Info | Tool to retrieve comprehensive company profiles and business intelligence. Use when you need detailed company information by name, domain, or email. |
| `INTERZOID_GET_COMPANY_MATCH_ADVANCED` | Get Company Match Advanced | Tool to generate a fuzzy-matching key for an organization name. Use when normalizing and deduplicating company names after extraction. |
| `INTERZOID_GET_COUNTRY_INFO` | Get Country Info | Tool to standardize a country name and return metadata like ISO codes, currency, TLD, and calling code. Use when you need detailed country information based on a country name or code. |
| `INTERZOID_GET_CURRENCY_RATE` | Get Currency Rate | Tool to retrieve live USD exchange rate for a currency symbol. Use when you need current market rate for a three-letter ISO 4217 currency. |
| `INTERZOID_GET_CUSTOM_DATA` | Get Custom Data | Tool to retrieve custom enriched data based on a topic and lookup value. Use after specifying the desired output fields. |
| `INTERZOID_GET_EMAIL_INFO` | Get Email Info | Tool to validate an email and return enrichment/demographics. Use when you need in-depth email analysis after confirming the email address. |
| `INTERZOID_GET_ENTITY_TYPE` | Get Entity Type | Tool to classify a text string into an entity type. Use when you need to identify if input refers to a Location, Organization, or Individual. |
| `INTERZOID_GET_EXECUTIVE_PROFILE` | Get Executive Profile | Tool to retrieve executive profile details based on company and title keywords. Use when you need executive information such as LinkedIn and biography links. |
| `INTERZOID_GET_FULL_NAME_MATCH` | Get Full Name Match | Tool to generate a similarity key for a full name. Use when performing fuzzy matching or deduplication of individual names. |
| `INTERZOID_GET_FULL_NAME_MATCH_SCORE` | Get Full Name Match Score | Tool to return a similarity score between two full names. Use when determining if two person names likely refer to the same individual. |
| `INTERZOID_GET_GLOBAL_ADDRESS_MATCH` | Get Global Address Match | Tool to generate a similarity key for a global address. Use when performing fuzzy matching and deduplication of international addresses. |
| `INTERZOID_GET_GLOBAL_PAGE_LOAD_PERFORMANCE` | Get Global Page Load Performance | Tool to measure page/API load time from a specified global origin. Use when benchmarking response times across geographic locations. |
| `INTERZOID_GET_GLOBAL_WEATHER` | Get Global Weather | Tool to return current weather conditions for a global location. Use when you need up-to-the-minute weather details for any city worldwide. |
| `INTERZOID_GET_IP_PROFILE` | Get IP Profile | Tool to retrieve IP intelligence including ASN, organization, geolocation, and reputation. Use when profiling an IP address for threat analysis. |
| `INTERZOID_GET_LICENSE` | Get API License Key | Tool to retrieve the configured Interzoid API license key. Use when you need to inspect which API key is active in the current connection. |
| `INTERZOID_GET_NAME_ORIGIN` | Get Name Origin | Tool to infer the likely country or region of origin from a personal name. Use after obtaining a name to guess its origin. |
| `INTERZOID_GET_ORG_MATCH_SCORE` | Get Org Match Score | Tool to return a 1–99 match score between two organization names. Use after gathering both names to evaluate organization similarity. |
| `INTERZOID_GET_ORG_STANDARD` | Get Org Standard | Tool to standardize an organization name to a canonical English form. Use when you need consistent company naming for data normalization. |
| `INTERZOID_GET_PARENT_COMPANY_INFO` | Get Parent Company Info | Tool to retrieve ultimate parent company information. Use when you have a company name or domain and need its ownership details. |
| `INTERZOID_GET_PHONE_PROFILE` | Get Phone Number Profile | Tool to retrieve phone number intelligence including validation, normalization, carrier, and risk assessment. Use when you need to enrich and validate a phone number after capture. |
| `INTERZOID_GET_PRODUCT_MATCH` | Get Product Match | Tool to generate a similarity key for a product name. Use when normalizing and fuzzy-matching names across catalogs. |
| `INTERZOID_GET_REMAINING_CREDITS` | Get Remaining API Credits | Tool to retrieve remaining Interzoid API credits. Use when you need to check your credit balance after usage. |
| `INTERZOID_GET_WEATHER_ZIPCODE` | Get Weather by ZIP Code | Tool to get current weather conditions for a US ZIP code. Use when you need real-time weather information for a specific area after confirming the ZIP code is valid. |
| `INTERZOID_IDENTIFY_LANGUAGE` | Identify Language | Tool to detect the language of a text string. Use when you need to identify the language of arbitrary text. Call after obtaining the text input. |
| `INTERZOID_TRANSLATE_TO_ANY` | Translate any text (auto-detect language) | Tool to auto-detect the input language and translate given text to the specified target language. Use when you need quick translations without specifying the source language. |

## Supported Triggers

None listed.

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

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

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

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 Interzoid 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, interzoid)
- 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 Interzoid 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=["interzoid"],
    )

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

  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 Interzoid 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 Interzoid
```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 Interzoid, 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=["interzoid"],
    )

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

  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 Interzoid 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 Interzoid to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Interzoid 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 Interzoid MCP Agent with another framework

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

## Related Toolkits

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- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
- [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.
- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.

## Frequently Asked Questions

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

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

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

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

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