How to Use the Agify MCP in LlamaIndex

Q: How do I load Agify tools into LlamaIndex?

Install llama-index-tools-mcp. Wrap your Vinkius HTTP endpoint in BasicMCPClient, then use McpToolSpec to expose the age prediction functions to your agent.

Index demographic estimates directly into your vector stores using LlamaIndex and the Agify MCP server.

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Connect Agify MCP to LlamaIndex

Create your Vinkius account to connect Agify 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 Agify with LlamaIndex

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Embed Age Estimates into RAG

The `predict_age` tool pulls demographic data that LlamaIndex instantly converts into searchable document nodes. Your function agent reads a historical text, extracts the names, and fetches their likely ages. Those estimates get embedded alongside the original text. When a user asks a question about the people in the story, the query engine retrieves the exact API data instead of guessing.

Bulk Indexing with Agify MCP Server

Building a knowledge base from a massive user directory demands the `predict_age_batch` tool. You pass a raw list of hundreds of first names directly to the MCP server. LlamaIndex receives the batch results and maps them back to your user profiles. You build a queryable index of your entire user base's estimated demographics in seconds.

Contextualize Global Documents

Parsing international records requires geographic context. The `predict_age` tool accepts a country parameter so your data agent can specify where the document originated. A name like "Andrea" generates a completely different age profile in Italy versus the United States. Your vector store retains this regional accuracy for future semantic searches.

Setup guide

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

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Common questions about Agify MCP in LlamaIndex

Install `llama-index-tools-mcp`. Wrap your Vinkius HTTP endpoint in `BasicMCPClient`, then use `McpToolSpec` to expose the age prediction functions to your agent.

Your FunctionAgent handles the routing. You ask a natural language question, and the agent decides whether to call the tool or search the existing vector index.

LlamaIndex persists the output. Instead of throwing away the age prediction after one use, you embed it into a knowledge base for future semantic retrieval.

Yes. You can extract an array of names during the ingestion phase and pass them to the batch prediction tool. The returned ages become metadata for your document nodes.

No data persists on the Vinkius infrastructure. The first names and geographic codes you send exist only in memory during the HTTP transport and are wiped the millisecond the response fires.

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