How to Use the Findymail MCP in LlamaIndex

Q: How do I filter Findymail results in LlamaIndex?

Use the searchleads parameters to narrow down your results before indexing. This ensures only your target ICP hits your vector database.

Index B2B lead data directly into LlamaIndex for searchable, grounded prospect intelligence.

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Connect Findymail MCP to LlamaIndex

Create your Vinkius account to connect Findymail 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 Findymail with LlamaIndex

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Turn Findymail results into knowledge

Use `search_leads` to pull raw prospect data and feed it into your LlamaIndex vector store. This turns flat API responses into a searchable knowledge base you can query later. Your agent can now answer questions about your lead list using semantic search. It finds patterns in your prospects that a simple database lookup would miss.

Ground your agent in fresh data

Connect `get_lead_search_data` as a tool that populates your index in real-time. Your RAG application stays current because your agent fetches new leads whenever the index needs an update. It stops your agent from hallucinating old contact info. The index reflects the live state of your search results, keeping your outbound efforts accurate.

Verify prospects before indexing

Run `verify_email` on every contact before adding it to your LlamaIndex collection. This prevents junk data from polluting your vector stores. Your index only contains valid, reachable leads. It makes your final retrieval steps much cleaner because you aren't wasting tokens on dead addresses.

Setup guide

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

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Common questions about Findymail MCP in LlamaIndex

You instantiate the MCP client and pass the tools to a FunctionAgent. This allows the agent to search for leads and index the relevant details automatically.

Absolutely. Once the tools populate your vector store, you can query your agent to find specific prospects based on the company data you've retrieved.

Yes. Your data is processed through your local MCP client and stored in your private vector index. No third party touches your prospect lists.

The server accesses business names, contact emails, and direct phone numbers. This raw information is then indexed into your local store for semantic retrieval.

Use the `search_leads` parameters to narrow down your results before indexing. This ensures only your target ICP hits your vector database.

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