How to Use the Snov.io MCP in LlamaIndex

Q: Can I use Snov.io for initial prospecting and verification with LlamaIndex?

Yes. You can run a sequence: find domain (domainsearch), then verify the email (emailverifier). Indexing these verified pairs means you build an internal, persistent knowledge graph of high-quality leads.

Q: Can I use Snov.io for managing prospect lists with LlamaIndex?

You can index the results from listprospectlists and even track changes using addprospect. This turns your simple list management into a queryable, historical record.

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Connect Snov.io MCP to LlamaIndex

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Setup Snov.io with LlamaIndex

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Building Searchable Prospect Indexes

Don't just run tools; index them. The MCP Server allows the output of any Snov.io tool to become part of a searchable vector store. For example, after running `tech_checker`, you can save the results into your knowledge base. Later, you query that data to remember what technologies were found for specific domains.

Retrieving Verified Contact Records

Need to know about a lead from last month? You'll use `get_prospect_by_email` and index the result. LlamaIndex lets you query this past API data semantically, so you can ask questions like, 'What was the tech stack for John Doe?' without needing the original record.

Monitoring Account Status in RAG Applications

The agent can index results from `get_balance` and `list_prospect_lists`. This means your RAG application doesn't just answer general questions; it answers specific business questions, like 'When was the last time we checked our credit balance?' or 'What lists are currently active?'

Setup guide

Set up Snov.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 Snov.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 Snov.io tools.",
)
response = await agent.run("List recent Snov.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 Snov.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 Snov.io MCP in LlamaIndex

LlamaIndex treats every tool call as a source of knowledge. The MCP Server allows you to index the output from tools like `email_finder` or `domain_search`, making them part of your searchable document corpus.

Yes. You can run a sequence: find domain (`domain_search`), then verify the email (`email_verifier`). Indexing these verified pairs means you build an internal, persistent knowledge graph of high-quality leads.

The server touches structured data: domain names, email formats, technological metadata (from `tech_checker`), and account metrics like credits. These are ideal sources for grounding an index.

The MCP Server provides verifiable API results, which is exactly what RAG applications need. You get answers grounded in real Snov.io data, not just model hallucination.

You can index the results from `list_prospect_lists` and even track changes using `add_prospect`. This turns your simple list management into a queryable, historical record.

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