How to Use the vCard Contacts Parser MCP in LlamaIndex

Q: How does LlamaIndex use the vCard Contacts Parser?

You run parsekindleclippings to generate JSON from contacts. Then, you pass that structured JSON output into your index. This makes the contact data part of a searchable knowledge base for future queries.

Build RAG applications that index results using LlamaIndex with the vCard Contacts Parser.

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Connect vCard Contacts Parser MCP to LlamaIndex

Create your Vinkius account to connect vCard Contacts Parser 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 vCard Contacts Parser with LlamaIndex

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Index structured contacts

The `parse_kindle_clippings` tool takes raw contact exports (like massive `.vcf` files) and converts them into clean JSON. This output becomes a new, searchable document in your LlamaIndex knowledge base. You can now query past sessions or configurations about specific contacts months later, retrieving answers grounded in the actual parsed data.

Search vCard Exports

When you run `parse_kindle_clippings`, it extracts all necessary details—names, phones, addresses—and indexes them. This allows your RAG application to perform semantic search on historical contact data. Forget general memory recall; LlamaIndex lets you query the specific facts from the vCard Contacts Parser output.

Process Raw Contact Files

The `parse_kindle_clippings` function processes raw exports, accurately separating names and phone numbers into distinct JSON fields. These clean data points are immediately available to be indexed for retrieval. This turns unstructured vCard text into highly usable knowledge chunks that improve the accuracy of your AI client's answers.

Setup guide

Set up vCard Contacts Parser 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 vCard Contacts Parser 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 vCard Contacts Parser tools.",
)
response = await agent.run("List recent vCard Contacts Parser 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 vcard-parser. 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 vCard Contacts Parser MCP in LlamaIndex

You run `parse_kindle_clippings` to generate JSON from contacts. Then, you pass that structured JSON output into your index. This makes the contact data part of a searchable knowledge base for future queries.

Yes. The tool is built to manage massive iPhone and Android `.vcf` exports efficiently. Indexing this data means your RAG application can search through huge volumes of contacts without issue.

It provides structured JSON containing individual contact details, including names, phone numbers, and addresses. These fields are perfect candidates for indexing because they are discrete facts.

No specific limits are mentioned; the tool is designed to handle massive exports, allowing you to build a comprehensive knowledge base of your contacts.

This server focuses on handling raw contact information and exports, resulting in structured JSON representations of contacts and addresses for indexing.

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