How to Use the Apollo.io MCP in LlamaIndex

Q: How do I convert Apollo.io tool outputs into LlamaIndex document nodes?

You run getcontact to fetch the raw JSON payload, which the LlamaIndex MCP tool spec converts into a standard LlamaIndex Document. This document is then split, embedded, and injected into your vector index for immediate RAG querying.

Q: Can LlamaIndex query my active Apollo.io sequences?

Yes, the agent calls listsequences to fetch your active campaigns and indexes their templates. This allows the query engine to match a prospect's profile with the most contextually relevant email sequence.

Q: Can I filter which Apollo.io MCP tools are exposed to my LlamaIndex agent?

Yes, you use the allowedtools filter during the McpToolSpec initialization to restrict access. For example, you can expose only searchpeople while hiding sequence enrollment tools.

Q: Where is my Apollo.io email account details data processed?

All data retrieved via listemailaccounts is processed locally within your isolated runtime environment. No credentials or email account details are stored on external servers, maintaining strict data privacy.

Index live Apollo.io sales data directly into LlamaIndex vector stores to ground your outbound agents in real-time context.

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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 live prospect data into your LlamaIndex RAG

This Apollo.io MCP Server lets LlamaIndex ingest real-time data from `get_contact` and `search_organizations` directly into your vector store. Instead of relying on static CSV exports, your LlamaIndex agent queries live customer profiles and indexes the text chunks for semantic retrieval. This live LlamaIndex indexing ensures your RAG pipeline always references current job titles and company sizes. When your LlamaIndex agent drafts an email, it pulls these fresh vector embeddings to ground the message in verified Apollo.io facts.

Ground outbound emails in historical call records

The `get_call` tool retrieves historical conversation logs that LlamaIndex converts into searchable text nodes. Your LlamaIndex agent queries these indexed logs to find past objections, pain points, and product feedback before drafting a follow-up. This mechanism prevents your LlamaIndex agent from repeating old pitches to existing Apollo.io contacts. By analyzing actual call outcomes, the LlamaIndex agent selects the absolute best campaign using `list_sequences` to continue the conversation naturally.

Map account hierarchies using semantic search

Your LlamaIndex agent uses `search_contacts` and `search_people` to discover key decision-makers and build a localized knowledge graph of target accounts. The LlamaIndex agent queries this graph to understand reporting structures and identify the best entry point. Once the LlamaIndex agent maps the accounts, it triggers `add_contact_to_sequence` for the selected stakeholders. This targeted LlamaIndex approach replaces high-volume spam with precise, account-based Apollo.io outreach.

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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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Apollo.io. 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 Apollo.io MCP in LlamaIndex

You run `get_contact` to fetch the raw JSON payload, which the LlamaIndex MCP tool spec converts into a standard LlamaIndex Document. This document is then split, embedded, and injected into your vector index for immediate RAG querying.

Yes, the agent calls `list_sequences` to fetch your active campaigns and indexes their templates. This allows the query engine to match a prospect's profile with the most contextually relevant email sequence.

The agent queries your vector store to check if a contact ID is already marked as active. If no active run exists, it executes `add_contact_to_sequence` to start the outreach.

Yes, you use the allowed_tools filter during the McpToolSpec initialization to restrict access. For example, you can expose only `search_people` while hiding sequence enrollment tools.

All data retrieved via `list_email_accounts` is processed locally within your isolated runtime environment. No credentials or email account details are stored on external servers, maintaining strict data privacy.

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