How to Use the Clearout MCP in LlamaIndex

Q: How does LlamaIndex store the results of a Clearout MCP check?

Your LlamaIndex agent takes the output from verifyemail and converts it into a document node. This node is then embedded and stored in your vector database, making verification metadata searchable.

Q: Can I query my Clearout credit status using LlamaIndex RAG?

Yes, you can index the output of getcredits into your knowledge base. This allows your agent to answer questions about your remaining balance by querying its indexed memory.

Q: How do I handle catch-all domains in Clearout using LlamaIndex?

Your agent runs verifycatchall to analyze the domain's behavior before indexing. LlamaIndex uses this metadata to tag nodes, letting you filter out risky domains during semantic search retrieval.

Q: Can I remove bulk lists from my remote storage?

Yes, the removebulklist tool allows your agent to delete processed lists after indexing. This keeps your storage clean and prevents duplicate file parsing.

Index verified lead data directly into your LlamaIndex vector stores using the Clearout MCP Server.

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Connect Clearout MCP to LlamaIndex

Create your Vinkius account to connect Clearout 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 Clearout with LlamaIndex

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Build a clean RAG knowledge base with LlamaIndex

The `verify_email` tool ensures only active, valid leads enter your LlamaIndex document index. Your agent checks incoming addresses before writing them to your vector store, preventing junk data from polluting semantic search results. This validation keeps your RAG applications grounded in accurate, real-world contact details. You avoid wasting embedding tokens on dead mailboxes and catch-all domains.

Index bulk verification outputs for search

The `download_results` tool fetches completed verification lists so LlamaIndex can parse and index the clean data. Your agent coordinates the process using `verify_bulk` and `get_bulk_status` before feeding the structured results into your local vector database via this MCP Server. Once indexed, you can query your lead database using natural language to find verified prospects. This lets your agent act on live, verified status updates instead of stale, unverified spreadsheets.

Find and index target accounts dynamically

The `find_email` MCP tool lets your LlamaIndex agent search for corporate emails and immediately index them as new nodes. Instead of static list loading, your agent discovers contacts dynamically during query execution and saves them to the active index. By calling `verify_catchall` on the domain first, the agent determines if the target address is reliable. This creates a self-cleaning knowledge base that updates itself during runtime.

Setup guide

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

Your LlamaIndex agent takes the output from `verify_email` and converts it into a document node. This node is then embedded and stored in your vector database, making verification metadata searchable.

Yes, you can index the output of `get_credits` into your knowledge base. This allows your agent to answer questions about your remaining balance by querying its indexed memory.

Your agent runs `verify_catchall` to analyze the domain's behavior before indexing. LlamaIndex uses this metadata to tag nodes, letting you filter out risky domains during semantic search retrieval.

Yes, the `remove_bulk_list` tool allows your agent to delete processed lists after indexing. This keeps your storage clean and prevents duplicate file parsing.

All email payloads are processed in memory within Vinkius's zero-trust runtime environment. The raw contact details are never stored on the MCP host, ensuring your proprietary lead lists remain confidential.

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