How to Use the Genderize MCP in LlamaIndex

Q: How do I configure Genderize with LlamaIndex?

Install llama-index-tools-mcp via pip. Set up the BasicMCPClient with your HTTP endpoint, wrap it in McpToolSpec, and pass the resulting tools to your FunctionAgent.

Q: How do regional tools improve LlamaIndex search?

Regional tools like estimategenderfrance provide localized probability scores. This ensures your vector index contains accurate demographic context for non-English source documents.

Index demographic name data directly into your LlamaIndex knowledge base with the Genderize MCP Server.

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Connect Genderize MCP to LlamaIndex

Create your Vinkius account to connect Genderize 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 Genderize with LlamaIndex

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Indexing Demographic Context

The `estimate_gender` tool allows LlamaIndex to query the Genderize API and embed the resulting probability scores into your vector store. Your agent extracts a name from a raw document, fetches the demographic metadata, and saves both as a unified node. That changes how your RAG applications handle user records. When you query your index later, the retrieval engine returns the original text alongside the statistical gender prediction, providing immediate context without requiring a live API call during the search.

Localized LlamaIndex MCP Server Queries

The `estimate_gender_brazil` and `estimate_gender_spain` tools let your indexing pipeline account for regional naming conventions. Your agent reads the country code from your source documents and selects the specific regional endpoint. This prevents inaccurate embeddings for names that vary by language. The RAG system stores the localized probability score, ensuring your semantic search results reflect the correct cultural context of the original data.

Batch Embedding Generation

The `estimate_genders_bulk` tool processes large lists of names before your LlamaIndex pipeline generates embeddings. You feed an array of unclassified names to the tool, and it returns the complete set of predictions in one response. You use `verify_api_connection` to confirm the external service is active before starting the batch job. The agent then maps the bulk results directly into your document nodes, minimizing the time required to build your searchable knowledge base.

Setup guide

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

Install `llama-index-tools-mcp` via pip. Set up the `BasicMCPClient` with your HTTP endpoint, wrap it in `McpToolSpec`, and pass the resulting tools to your FunctionAgent.

Yes. Once the agent retrieves the prediction, LlamaIndex embeds the result into your vector store. Subsequent queries read from the index instead of hitting the external API again.

The agent uses the tools to generate metadata tags for your documents. You then apply standard LlamaIndex filters to search specifically for records matching a certain probability threshold.

Regional tools like `estimate_gender_france` provide localized probability scores. This ensures your vector index contains accurate demographic context for non-English source documents.

The system only sends the isolated first name string to the API endpoint. The protocol enforces strict boundaries, meaning your underlying document index remains completely hidden from the external demographic service.

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