How to Use the Hunter MCP in LlamaIndex

Q: How does LlamaIndex store the email addresses found by Hunter?

The framework takes the JSON output from emailfinder and converts it into document nodes. These nodes are then embedded and stored in your vector database for quick semantic retrieval.

Q: Can I query my Hunter campaign metrics directly in LlamaIndex?

Yes, by using listcampaigns and indexing the output, you can ask natural language questions about your open rates. The agent searches the index to give you exact numbers.

Q: Does the Hunter MCP Server support real-time data verification in LlamaIndex?

Yes, the agent calls emailverifier on demand whenever it retrieves a contact from your vector store. This ensures you never send an email to an address that went inactive after you indexed it.

Q: How do I limit API usage when running semantic searches?

You run emailcount first to check the size of a domain's contact list before indexing. This prevents your agent from pulling massive domains that would drain your API budget.

Index live Hunter MCP Server domain and email verification data directly into your LlamaIndex vector store for grounded RAG.

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Connect Hunter MCP to LlamaIndex

Create your Vinkius account to connect Hunter to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Hunter with LlamaIndex

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Index domain prospects into LlamaIndex

The `domain_search` tool retrieves professional email patterns and contacts, which LlamaIndex immediately parses into searchable document nodes. Your agent queries this local vector index to find the right point of contact instead of making repeated, costly API calls. By storing these results, your RAG applications stay grounded in verified organizational structures. You avoid hallucinated contact names because your agent pulls directly from the indexed Hunter data.

Query Hunter MCP Server leads using LlamaIndex

The `list_leads` tool extracts your saved prospects so LlamaIndex can build a semantic index of your sales pipeline. Your agent can then query this index to match incoming customer inquiries with existing target accounts. Integrating `list_lead_lists` lets the framework categorize contacts based on semantic similarity. You can ask your agent who your most relevant leads are, and it will answer using live, verified data.

Analyze campaign performance with semantic search

The `get_campaign` tool fetches performance metrics that LlamaIndex indexes alongside your outreach templates. Your agent can query past campaigns to see which subject lines yielded the lowest bounce rates. Using `email_verifier` before indexing ensures that only deliverable addresses enter your knowledge base. This keeps your local database clean and prevents your agent from referencing outdated contact info.

Setup guide

Set up Hunter MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Hunter MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Hunter tools.",
)
response = await agent.run("List recent Hunter data")

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Common questions about Hunter MCP in LlamaIndex

The framework takes the JSON output from `email_finder` and converts it into document nodes. These nodes are then embedded and stored in your vector database for quick semantic retrieval.

Yes, by using `list_campaigns` and indexing the output, you can ask natural language questions about your open rates. The agent searches the index to give you exact numbers.

Yes, the agent calls `email_verifier` on demand whenever it retrieves a contact from your vector store. This ensures you never send an email to an address that went inactive after you indexed it.

You run `email_count` first to check the size of a domain's contact list before indexing. This prevents your agent from pulling massive domains that would drain your API budget.

Every API call to fetch domain data runs through isolated Vinkius sandboxes that destroy all session logs upon completion. Your retrieved contact records are stored directly in your local vector database, never on our infrastructure.

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