How to Use the AeroLeads MCP in LlamaIndex

Q: How do I configure LlamaIndex to use the AeroLeads MCP Server?

Install llama-index-tools-mcp via pip. Create a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and call totoollistasync() to pass the tools to your FunctionAgent.

Q: Can LlamaIndex query my existing AeroLeads lists?

Yes. The agent executes listprospectinglists to view your current folders. It then pulls the contents and indexes them for natural language querying.

Q: Does the AeroLeads LlamaIndex integration support data access filters?

You set includeresources=True during setup. This allows the agent to read underlying data structures while restricting write access if you exclude tools like addnewlead.

Q: How do I find specific domains using this setup?

Prompt your agent to trigger searchdomainleads for a target company. The model parses the returned JSON and embeds the potential contacts into your active vector store.

Turn raw AeroLeads contact data into a searchable vector index with LlamaIndex for grounded sales intelligence.

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Connect AeroLeads MCP to LlamaIndex

Create your Vinkius account to connect AeroLeads 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 AeroLeads with LlamaIndex

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Index your B2B prospects

Connect your LlamaIndex application to live sales data via this MCP Server. Instead of just returning a list of names, your agent calls `list_captured_leads` and embeds the results directly into a vector store. This turns static contact lists into a dynamic knowledge base. You ingest thousands of records and make them instantly available for semantic search across your entire sales organization.

Query lead data via LlamaIndex

Sales reps don't always know exactly what to search for. They ask questions like 'Who are the engineering leaders at Acme Corp?' Your FunctionAgent uses `get_linkedin_details` to pull the profile data, then synthesizes an answer grounded in the actual API response. Hallucinations drop to zero because the model relies entirely on retrieved facts. If a rep asks about a specific contact, the system runs `get_lead_details` and returns the exact verified phone number or email address on file.

Build dynamic RAG pipelines with this MCP Server

Static documents go out of date the minute you upload them. By connecting `McpToolSpec`, your RAG application pulls live team information using `list_team_members` and active webhook configurations via `list_webhooks`. The agent filters out irrelevant tools using the `allowed_tools` parameter. You maintain strict control over which endpoints the application can hit, keeping the vector store focused strictly on approved sales intelligence.

Setup guide

Set up AeroLeads 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 AeroLeads 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 AeroLeads tools.",
)
response = await agent.run("List recent AeroLeads data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AeroLeads. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AeroLeads MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Create a `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and call `to_tool_list_async()` to pass the tools to your FunctionAgent.

Yes. The agent executes `list_prospecting_lists` to view your current folders. It then pulls the contents and indexes them for natural language querying.

You set `include_resources=True` during setup. This allows the agent to read underlying data structures while restricting write access if you exclude tools like `add_new_lead`.

Prompt your agent to trigger `search_domain_leads` for a target company. The model parses the returned JSON and embeds the potential contacts into your active vector store.

All operations happen within a zero-trust architecture. When the system retrieves LinkedIn profiles and verified business emails, the payload travels through an isolated sandbox that retains absolutely zero state.

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