How to Use the Listclean MCP in LlamaIndex

Q: How do I verify single emails in LlamaIndex using Listclean?

Use the verifysingleemail tool within your LlamaIndex agent to check individual addresses in real-time. The tool returns validation data that you can immediately index or use to route your queries.

Q: Can I clean bulk email lists in LlamaIndex with Listclean?

Yes, you can run verifybatchemails to clean up to 3,000 emails before indexing them. This ensures your LlamaIndex documents contain only deliverable contact information.

Q: How do I check my remaining Listclean credits in LlamaIndex?

Call checkaccountcredits within your LlamaIndex pipeline to retrieve your remaining verification balance. This helps your agent manage resources before initiating large-scale indexing runs.

Q: How do I pull verification history into my index?

Use getverificationlogs to fetch your past validation logs directly from the MCP Server. LlamaIndex can index these logs so your query engine can search through past verification outcomes.

Index verified email data into LlamaIndex to keep your knowledge bases clean and accurate.

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Create your Vinkius account to connect Listclean 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 Listclean with LlamaIndex

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Verify and index emails with LlamaIndex

The `verify_single_email` tool validates individual email addresses before they are indexed into your LlamaIndex vector store. Your index stays free of synthetic or dead email addresses, ensuring that semantic queries return actual, active contacts. This prevents your RAG applications from retrieving outdated or fake contact information. Grounding your data in verified emails via our MCP Server means your agent makes decisions based on real people.

Bulk verification for clean LlamaIndex documents

The `verify_batch_emails` tool cleans up to 3,000 emails simultaneously before you build or update your LlamaIndex document index using this MCP integration. Your ingestion pipeline runs this batch check to weed out bad contacts from raw text files or CSVs. This keeps your vector search clean and highly relevant. By filtering out invalid addresses at the ingestion stage, you avoid indexing noise that dilutes your search results.

Audit your validation history via MCP Server tools

The `get_verification_logs` tool pulls past verification records directly into your LlamaIndex query engine. Your agent can search through these logs to trace when and why an email address was flagged as invalid. You can also call `get_account_profile` to fetch account details and index them for resource planning. Maintaining an accurate log history protects your downstream search results from stale data.

Setup guide

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

Use the `verify_single_email` tool within your LlamaIndex agent to check individual addresses in real-time. The tool returns validation data that you can immediately index or use to route your queries.

Yes, you can run `verify_batch_emails` to clean up to 3,000 emails before indexing them. This ensures your LlamaIndex documents contain only deliverable contact information.

Call `check_account_credits` within your LlamaIndex pipeline to retrieve your remaining verification balance. This helps your agent manage resources before initiating large-scale indexing runs.

Use `get_verification_logs` to fetch your past validation logs directly from the MCP Server. LlamaIndex can index these logs so your query engine can search through past verification outcomes.

Your email addresses are processed in transit and are not stored in the Vinkius sandbox environment. Only the verified metadata is returned to your LlamaIndex vector store, keeping your contact database secure.

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