How to Use the NumVerify MCP in LlamaIndex

Q: How do I install the NumVerify MCP Server in LlamaIndex?

Install llama-index-tools-mcp using pip. Create a BasicMCPClient pointing to your server URL, wrap it in a McpToolSpec, and call totoollistasync() to load the tools into your agent.

Q: Can LlamaIndex store NumVerify outputs for semantic search?

Yes, the agent runs getphonecarrier or other validation tools and writes the resulting JSON directly into your document index. This lets you perform semantic searches over phone carrier and location data.

Q: How does NumVerify improve RAG accuracy in LlamaIndex?

It provides factual, real-time telecom data via validatephone. By indexing these verified details, your RAG system answers user queries using live carrier and location data instead of guessing.

Q: Can I filter which phone tools the indexer can access?

Yes, you can use the allowedtools filter when initializing your tool spec. This lets you restrict your agent to only getphonelinetype if you want to avoid running full carrier lookups.

Index phone metadata directly into LlamaIndex vector stores for semantic search and grounded RAG applications.

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Connect NumVerify MCP to LlamaIndex

Create your Vinkius account to connect NumVerify 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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Feed `validate_phone` results into your index

The `validate_phone` tool parses and checks phone numbers to ensure they are active and structured correctly. LlamaIndex takes this output and indexes it directly into your vector store. This creates a searchable, grounded database of verified user contact information. Instead of relying on static database queries, your query engine searches through this indexed metadata. If an agent needs to confirm a user's status, it queries the index to find the last verified timestamp and format details.

Build RAG workflows with `get_phone_line_type`

The `get_phone_line_type` tool identifies whether a number is a mobile line, landline, or virtual VoIP service. Your LlamaIndex RAG application uses this classification to enrich user profiles before saving them to your knowledge base. When users ask questions about their account details, the system pulls data from this index. The agent knows exactly which communication channels are valid based on the stored line type. This prevents the LLM from hallucinating contact methods that do not exist.

Geographic search using this MCP Server

This MCP Server provides geographic lookup tools to locate where a phone number is registered. The `get_phone_location` tool extracts city, state, and country data. LlamaIndex indexes these location profiles to allow semantic queries based on geography. You can ask your index to list all users located in a specific region. The query engine looks up the indexed outputs of `get_phone_carrier` and location tools to construct a precise list of regional carrier distributions.

Setup guide

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

Install `llama-index-tools-mcp` using pip. Create a `BasicMCPClient` pointing to your server URL, wrap it in a `McpToolSpec`, and call `to_tool_list_async()` to load the tools into your agent.

Yes, the agent runs `get_phone_carrier` or other validation tools and writes the resulting JSON directly into your document index. This lets you perform semantic searches over phone carrier and location data.

It provides factual, real-time telecom data via `validate_phone`. By indexing these verified details, your RAG system answers user queries using live carrier and location data instead of guessing.

Yes, you can use the `allowed_tools` filter when initializing your tool spec. This lets you restrict your agent to only `get_phone_line_type` if you want to avoid running full carrier lookups.

Phone numbers, carrier details, and location records are processed in memory to execute the lookup tools. The Vinkius sandbox ensures that no phone numbers are persisted on disk after the session ends.

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