How to Use the NeverBounce MCP in LlamaIndex

Q: How do I connect LlamaIndex to the NeverBounce MCP Server?

Use the llama-index-tools-mcp package. Initialize a BasicMCPClient with your endpoint, wrap it in an McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: How do I filter which NeverBounce MCP tools LlamaIndex can access?

Use the allowedtools parameter when creating your tool spec. You might restrict an agent to just checking single emails while blocking access to bulk job creation.

Index clean email lists directly into your LlamaIndex knowledge base using the NeverBounce MCP Server.

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Connect NeverBounce MCP to LlamaIndex

Create your Vinkius account to connect NeverBounce 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 NeverBounce with LlamaIndex

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Clean RAG data with this MCP Server

Bad contact data poisons your vector stores. Before your pipeline indexes a new lead list, it calls `check_email`. The agent filters out the bounces before they ever reach your database. This creates a self-healing knowledge base. When a user asks your agent to find valid contacts for a campaign, it searches the index knowing every address already passed verification.

Index bulk verification results

You have thousands of old contacts sitting in cold storage. Feed a CSV link to `create_job_from_url`. The system processes the massive file in the background. Your LlamaIndex application waits for the green light using `get_job_status`. When ready, `get_job_results` fetches the parsed data. The framework then chunks and embeds only the safe addresses into your semantic search engine.

Audit your verification history

Sometimes you need to know exactly what ran last week. Your agent pulls historical data using `list_jobs`. It reads the metadata and adds it to your index. This gives you a searchable audit trail. If someone asks why a specific campaign bounced, the agent queries the index, finds the relevant `parse_job` output, and tells you exactly when the list was checked.

Setup guide

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

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Common questions about NeverBounce MCP in LlamaIndex

Use the `llama-index-tools-mcp` package. Initialize a `BasicMCPClient` with your endpoint, wrap it in an `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Yes. Once your agent retrieves the data via the tools, it chunks the returned JSON. Those chunks become searchable nodes in your vector store.

Use the `allowed_tools` parameter when creating your tool spec. You might restrict an agent to just checking single emails while blocking access to bulk job creation.

You write a loop where the agent calls the status check tool periodically. Once it sees a completed state, it moves on to fetch the actual rows.

Vinkius operates on a zero-trust architecture. The raw contact lists and job metadata pass through ephemeral sandboxes that destroy themselves after the request finishes.

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