Hunter MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create New Lead, Enrich Email Data, Find Person Email, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Hunter as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Hunter app connector for LlamaIndex is a standout in the Sales Automation category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Hunter. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Hunter?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Hunter MCP Server
Connect your Hunter account to any AI agent and power your email prospecting through natural conversation.
LlamaIndex agents combine Hunter tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Domain Search — Find all professional email addresses associated with a domain or company name
- Email Finder — Discover the most likely email address for a specific person by name and company
- Email Verification — Check the validity and deliverability of any email address with confidence scores
- Email Count — Check how many email addresses are available for a domain before searching
- Contact Enrichment — Retrieve all available professional data (title, company, social profiles) for an email address
- Lead Management — Create, list, update, and delete leads in your Hunter CRM with lead list organization
- Account Monitoring — Track remaining API credits and account usage
The Hunter MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 Hunter tools available for LlamaIndex
When LlamaIndex connects to Hunter through Vinkius, your AI agent gets direct access to every tool listed below — spanning email-finder, domain-search, email-verification, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Save lead to CRM
Get contact intel
Find personal email
Check credits
Check email availability
Get lead info
List lead lists
List lead profiles
Delete lead record
Find emails for domain
Modify lead data
Check deliverability
Connect Hunter to LlamaIndex via MCP
Follow these steps to wire Hunter into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Hunter MCP Server
LlamaIndex provides unique advantages when paired with Hunter through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Hunter tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Hunter tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Hunter, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Hunter tools were called, what data was returned, and how it influenced the final answer
Hunter + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Hunter MCP Server delivers measurable value.
Hybrid search: combine Hunter real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Hunter to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Hunter for fresh data
Analytical workflows: chain Hunter queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Hunter in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Hunter immediately.
"Find all emails at stripe.com and verify the CTO's email address."
"Find the email for Sarah Chen at Acme Corp and enrich her contact data."
"Check my Hunter account credits and list all saved leads."
Troubleshooting Hunter MCP Server with LlamaIndex
Common issues when connecting Hunter to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHunter + LlamaIndex FAQ
Common questions about integrating Hunter MCP Server with LlamaIndex.