# How to integrate Emaillistverify MCP with LlamaIndex

```json
{
  "title": "How to integrate Emaillistverify MCP with LlamaIndex",
  "toolkit": "Emaillistverify",
  "toolkit_slug": "emaillistverify",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/emaillistverify/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/emaillistverify/framework/llama-index.md",
  "updated_at": "2026-05-12T10:10:14.160Z"
}
```

## Introduction

This guide walks you through connecting Emaillistverify to LlamaIndex using the Composio tool router. By the end, you'll have a working Emaillistverify agent that can check if this email address is valid, get detailed deliverability info for an email, verify a user's email before signup through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Emaillistverify account through Composio's Emaillistverify MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Emaillistverify with

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

The Emaillistverify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Emaillistverify account. It provides structured and secure access to your email verification tools, so your agent can validate addresses, check deliverability, and provide detailed insights into email list quality on your behalf.
- Real-time email verification: Instantly check if a single email address is valid and deliverable before adding it to your list or sending messages.
- Detailed email validation insights: Get in-depth reports on why an email address may be risky or undeliverable, including error types and validation reasons.
- Automated list hygiene: Quickly verify new signups or leads as they come in, helping you keep your email lists clean and up-to-date.
- Prevent bounced emails: Reduce hard bounces and protect your sender reputation by validating addresses before campaigns are sent.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `EMAILLISTVERIFY_CHECK_BLACKLISTS` | Check Blacklists | Tool to check an IP address (IPv4/IPv6) or domain against multiple DNS-based blacklists (DNSBLs) for spam or malicious activity. Use when you need to verify the reputation of an IP or domain. Rate limit: 10 requests/second. |
| `EMAILLISTVERIFY_CHECK_DISPOSABLE` | Check Disposable Domain | Tool to verify if an email domain is associated with temporary/disposable email addresses. Includes DNS record verification. Use when you need to validate if a domain is disposable before accepting email registrations. |
| `EMAILLISTVERIFY_DELETE_MAILLIST` | Delete Maillist | Tool to delete a finished email list. Use when you need to remove a completed verification list. Only lists that have completed verification can be deleted. |
| `EMAILLISTVERIFY_DOWNLOAD_MAILLIST` | Download Email List | Tool to download a finished email list with verification results. Supports customizable columns (firstName, lastName, gender, result, etc.) and file format (csv/xlsx). Rate limit: 5 requests/second. |
| `EMAILLISTVERIFY_FIND_CONTACT` | Find Contact Email | Tool to search for a contact's business email address by name and company domain. Returns possible emails with confidence levels (high/medium/low/unknown). Rate limit: 5 requests/second. Credits: 5 (with name) or 10 (domain only). |
| `EMAILLISTVERIFY_GET_API_FILE_INFO` | Get API File Info | Tool to retrieve progress of an uploaded email list verification. Returns status (errored/waiting/progress/finished) with download URLs when complete. |
| `EMAILLISTVERIFY_GET_CREDITS` | Get Credits | Tool to retrieve details about available on-demand and subscription credits. Use when you need to check credit balance before performing verifications. On-demand credits never expire, subscription credits are refreshed daily. Rate limit: 10 requests/second. |
| `EMAILLISTVERIFY_GET_EMAIL_JOB` | Get Email Job Status | Tool to get the status of an asynchronous email verification job. Use when you need to check if a verification job has completed and retrieve its results. Rate limit: 100 requests per second. |
| `EMAILLISTVERIFY_GET_MAILLIST_PROGRESS` | Get Maillist Progress | Tool to retrieve real-time progress updates for an uploaded email list verification. Shows status, completion percentage, and credit usage. Rate limit: 100 requests/second. |
| `EMAILLISTVERIFY_UPLOAD_EMAIL_LIST` | Upload Email List | Tool to upload an email list file for bulk verification. Accepts .csv, .txt, or .xlsx files (max 100MB, 1M rows). Returns an ID to query verification progress. Rate limit: 5 requests/second. |
| `EMAILLISTVERIFY_VERIFY_SINGLE_EMAIL` | Verify Single Email | Tool to verify email deliverability status of a single email address. Returns a plain text status representing deliverability. Rate limit: 10 requests/second. Credits required: 1. |
| `EMAILLISTVERIFY_VERIFY_SINGLE_EMAIL_DETAILED` | Verify Single Email Detailed | Tool to verify email deliverability with detailed metadata including MX server info, ESP, first/last name estimation, gender, and role detection. Use when you need comprehensive email validation beyond basic deliverability. Rate limit: 10 requests/second, requires 1 credit per verification. |

## Supported Triggers

None listed.

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

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

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

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 Emaillistverify 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, emaillistverify)
- 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 Emaillistverify 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=["emaillistverify"],
    )

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

  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 Emaillistverify 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 Emaillistverify
```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 Emaillistverify, 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=["emaillistverify"],
    )

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

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

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

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## Frequently Asked Questions

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

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

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

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

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)