Genderize MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Genderize as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Genderize. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Genderize?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Genderize MCP Server
Connect your AI agent to the Genderize.io database to automate gender estimation through the Model Context Protocol (MCP). Genderize.io is a specialized API that provides statistical probabilities for the gender associated with any first name, backed by a database of over 114 million records. This MCP server enables you to estimate genders for single or multiple names, localized by country, directly through natural conversation.
LlamaIndex agents combine Genderize tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Gender Estimation — Predict whether a first name is associated with a male or female identity based on global data.
- Statistical Probability — Retrieve certainty scores (0.0 to 1.0) and the total data count used for each prediction.
- Country Localization — Localize results by providing ISO country codes (e.g., 'US', 'BR', 'GB') to improve accuracy for regional naming patterns.
- Batch Processing — Estimate genders for up to 10 names in a single request to process lead lists faster.
- Regional Helpers — Quickly check names for specific countries like the USA, Brazil, UK, Spain, and France using dedicated tools.
- No-Auth Free Tier — Start using the service immediately with up to 100 free requests per day without an API key.
- Scale with API Keys — Optionally provide an API key to access higher rate limits for large-scale data enrichment.
The Genderize MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Genderize to LlamaIndex via MCP
Follow these steps to integrate the Genderize MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Genderize
Why Use LlamaIndex with the Genderize MCP Server
LlamaIndex provides unique advantages when paired with Genderize through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Genderize tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Genderize tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Genderize, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Genderize tools were called, what data was returned, and how it influenced the final answer
Genderize + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Genderize MCP Server delivers measurable value.
Hybrid search: combine Genderize real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Genderize to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Genderize for fresh data
Analytical workflows: chain Genderize queries with LlamaIndex's data connectors to build multi-source analytical reports
Genderize MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Genderize to LlamaIndex via MCP:
estimate_gender
Predict gender by name
estimate_gender_brazil
Predict gender (Brazil)
estimate_gender_france
Predict gender (France)
estimate_gender_spain
Predict gender (Spain)
estimate_gender_uk
Predict gender (UK)
estimate_gender_us
Predict gender (USA)
estimate_genders_bulk
Predict multiple names
verify_api_connection
io API connectivity. Check connection
Example Prompts for Genderize in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Genderize immediately.
"Estimate the gender for the name 'Peter'."
"Predict the genders for these names: ['Alice', 'Bob', 'Charlie']."
"What is the predicted gender for 'Sasha' in Russia (RU)?"
Troubleshooting Genderize MCP Server with LlamaIndex
Common issues when connecting Genderize to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGenderize + LlamaIndex FAQ
Common questions about integrating Genderize MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Genderize with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Genderize to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.