# How to integrate Hunter MCP with LlamaIndex

```json
{
  "title": "How to integrate Hunter MCP with LlamaIndex",
  "toolkit": "Hunter",
  "toolkit_slug": "hunter",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/hunter/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/hunter/framework/llama-index.md",
  "updated_at": "2026-05-12T10:15:24.853Z"
}
```

## Introduction

This guide walks you through connecting Hunter to LlamaIndex using the Composio tool router. By the end, you'll have a working Hunter agent that can find all public emails at acme.com, enrich company details for tesla.com, create new lead with given info through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Hunter account through Composio's Hunter MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Hunter with

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

The Hunter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Hunter account. It provides structured and secure access to your lead generation and enrichment tools, so your agent can perform actions like finding emails, enriching company data, managing leads, and organizing leads lists on your behalf.
- Email discovery and search: Instantly ask your agent to find all public email addresses for a given company or domain, complete with metadata to fuel your outreach and marketing campaigns.
- Smart lead creation and management: Let your agent add new leads, update lead details, or delete outdated entries to keep your Hunter account organized and up-to-date.
- Company and contact enrichment: Have the agent fetch detailed company profiles or use the Email Finder to infer the best contact email for a specific person at a target company.
- Leads list organization: Direct your agent to create, update, or remove custom leads lists—making it easy to segment prospects for personalized marketing or sales workflows.
- Custom attribute management: Empower your agent to create or delete custom lead attributes, tailoring your CRM data fields to match your unique business needs.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `HUNTER_ACCOUNT_INFORMATION` | Account Information | Tool to retrieve information about your Hunter account. Use when you need to check your plan details and usage limits after confirming credentials. Returns `searches.available` and `verifications.available` fields among others; check these before bulk operations to avoid quota exhaustion. |
| `HUNTER_COMBINED_ENRICHMENT` | Combined Enrichment | Tool to find both person and company information from an email address or LinkedIn handle in a single request. Use when you need complete professional profile enrichment including employment and company details. |
| `HUNTER_COMPANY_ENRICHMENT` | Company Enrichment | Tool to get enrichment information for a company by its domain. Use when you need full company details (industry, description, location, metrics) from Hunter. |
| `HUNTER_CREATE_CUSTOM_ATTRIBUTE` | Create custom lead attribute | Tool to create a new custom lead attribute in your account. Use after deciding on the attribute label. |
| `HUNTER_CREATE_LEAD` | Create Lead | Tool to create a new lead. Use after gathering all prospect details to save them to your Hunter account. |
| `HUNTER_CREATE_LEADS_LIST` | Create Leads List | Tool to create a new leads list. Use when you need to organize leads into a custom list before adding leads. |
| `HUNTER_DELETE_CUSTOM_ATTRIBUTE` | Delete Custom Attribute | Tool to delete an existing custom attribute. Use after confirming the attribute ID to be removed. |
| `HUNTER_DELETE_LEAD` | Delete Lead | Tool to delete a lead. Use after confirming the lead's ID to remove it from your Hunter.io account. |
| `HUNTER_DELETE_LEADS_LIST` | Delete Leads List | Tool to delete a leads list by its ID. Use after confirming the leads list ID to remove it from your Hunter.io account. |
| `HUNTER_DISCOVER_COMPANIES` | Discover Companies | Tool to search and retrieve companies matching specified criteria using filters or natural language queries. Use when you need to discover companies from Hunter's B2B dataset based on industry, location, size, or other characteristics. |
| `HUNTER_DOMAIN_SEARCH` | Domain Search | Tool to search all email addresses for a given domain or company. Use when you need public emails and metadata for outreach or enrichment. Rate-limited; HTTP 429 returned on excess requests — honor the Retry-After header. |
| `HUNTER_EMAIL_COUNT` | Email Count | Tool to get the total number of email addresses Hunter has for a domain or company with breakdowns by type, department, and seniority. Use when you need email volume statistics without consuming API credits (this call is free). |
| `HUNTER_EMAIL_FINDER` | Email Finder | Tool to find the most likely email address for a person at a domain or company. Use when you have a person's name and a domain or company and need to infer their email. Results include a confidence score and status; treat emails with status 'accept_all' or 'risky' as lower reliability. Each call consumes API credits — avoid re-enriching the same contact. |
| `HUNTER_EMAIL_VERIFIER` | Email Verifier | Tool to verify the deliverability of an email address. Use when you need to ensure an address is valid and reachable. Response may include statuses `accept_all` or `risky`, indicating uncertain deliverability; do not treat these as fully valid without explicit review. For bulk verification, honor `Retry-After` headers on HTTP 429 responses and use exponential backoff. |
| `HUNTER_GET_CUSTOM_ATTRIBUTE` | Get Custom Attribute | Tool to retrieve details of a specific custom attribute. Use when you need the label and slug for an attribute ID. |
| `HUNTER_GET_LEAD` | Get Lead | Tool to retrieve details of a specific lead by ID. Use after confirming the lead's ID to fetch its full record. |
| `HUNTER_GET_LEADS_LIST` | Get Leads List | Tool to retrieve details of a specific leads list by ID. Use when you need to inspect the contents of an existing leads list. |
| `HUNTER_LIST_CAMPAIGNS` | List Campaigns | Tool to get all email campaigns in your Hunter account. Campaigns are returned in reverse-chronological order by creation date. Use when you need to retrieve and filter campaigns by status (started/archived) with pagination support. |
| `HUNTER_LIST_CUSTOM_ATTRIBUTES` | List Custom Attributes | Tool to list all custom lead attributes in your account. Use when you need to retrieve your account's custom lead attributes after authenticating. |
| `HUNTER_LIST_LEADS` | List Leads | Tool to list all leads saved in your account with optional filters. Use when you need to retrieve leads with specific criteria after confirming your API key. |
| `HUNTER_LIST_LEADS_LISTS` | List Leads Lists | Tool to list all leads lists in your account. Use when you need to retrieve and paginate through your leads lists. |
| `HUNTER_PEOPLE_ENRICHMENT` | People Enrichment | Tool to find all information associated with an email address or LinkedIn profile including name, location, job title and social handles. Use when you need to enrich contact data with additional personal and professional details. |
| `HUNTER_UPDATE_CUSTOM_ATTRIBUTE` | Update Custom Attribute | Tool to update an existing custom attribute's label. Use when renaming a custom attribute after creation. |
| `HUNTER_UPDATE_LEAD` | Update Lead | Tool to update details of an existing lead by ID. Use when you need to modify saved lead attributes after creation. |
| `HUNTER_UPDATE_LEADS_LIST` | Update Leads List | Tool to update the name of a specific leads list. Use when renaming an existing leads list. |
| `HUNTER_UPSERT_LEAD` | Upsert Lead | Tool to create or update a lead by email in one call. Use when you want to ensure a lead exists with the provided information without checking its existence first. |

## Supported Triggers

None listed.

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

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

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

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 Hunter 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, hunter)
- 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 Hunter 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=["hunter"],
    )

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

  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 Hunter 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 Hunter
```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 Hunter, 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=["hunter"],
    )

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

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

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

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- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active.
- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.
- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
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- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.

## Frequently Asked Questions

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

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

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

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

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