How to Use the Apollo.io MCP in LlamaIndex

Q: How do I limit which Apollo.io tools LlamaIndex sees?

You use the allowedtools filter when setting up your tool specification. This restricts the agent to only the functions you choose.

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Connect Apollo.io MCP to LlamaIndex

Create your Vinkius account to connect Apollo.io 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 Apollo.io with LlamaIndex

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Index your prospect data

Pipe the results of `search_people` into your LlamaIndex vector store. This turns flat contact records into a searchable knowledge base you can query with natural language. It changes how you find leads. You aren't just searching names; you're querying your entire history of Apollo.io data for specific prospect attributes.

Contextual lead enrichment

Connect your index to `enrich_company_data` to pull fresh metadata when answering user queries. The agent retrieves the latest firmographic details to ground its response in facts. This prevents outdated info from poisoning your search. Every answer reflects the current state of the organization as pulled from the API.

Automated sequence management

Your agent manages your sales flow by calling `list_sales_sequences` to audit your current outreach efforts. It uses `add_to_sequence` to enroll prospects based on the context found in your vector store. It removes the need for manual list management. The system identifies the opportunity and takes the next step based on its internal knowledge.

Setup guide

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

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Common questions about Apollo.io MCP in LlamaIndex

It fetches the data using the MCP server and indexes the text results into a vector store. This makes the information available for semantic retrieval.

Yes. The agent performs a RAG operation, combining your existing documents with the real-time data returned by the tools.

It does. You map the tools to a FunctionAgent, which then uses them to resolve user requests by searching or enriching data.

You use the allowed_tools filter when setting up your tool specification. This restricts the agent to only the functions you choose.

Your data resides in your local index. Only authorized queries can access the stored contact details, and all communication is encrypted.

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