Namsor MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Parse Full Name, Predict Country, Predict Diaspora, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Namsor 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 for LlamaIndex
The Namsor MCP Server for LlamaIndex is a standout in the Marketing Automation category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport 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 Namsor. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Namsor?"
)
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 Namsor MCP Server
Connect your Namsor account to any AI agent and simplify your name analytics and demographic enrichment through natural conversation.
LlamaIndex agents combine Namsor tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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.
What you can do
- Gender Prediction — Analyze first and last names to determine gender with high accuracy and probability metrics
- Origin & Residency — Predict a name's country of origin and likely current country of residency
- Ethnicity Tracking — Query US-specific ethnicity models (Hispanic, Asian, Black, White) for localized demographic insights
- Name Parsing — Break down complex full name strings into structured components (prefix, first, last, suffix)
- Diaspora Insights — Predict the diaspora group or ethnic cluster for a name within a specific country context
The Namsor MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Namsor tools available for LlamaIndex
When LlamaIndex connects to Namsor through Vinkius, your AI agent gets direct access to every tool listed below — spanning demographic-analysis, gender-prediction, ethnicity-tracking, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Parse full name on Namsor
Parse a full name string
Predict country on Namsor
Predict country residency from name
Predict diaspora on Namsor
Predict diaspora from name
Predict ethnicity on Namsor
g., Hispanic, Asian, White). Predict US ethnicity from name
Predict gender on Namsor
Predict gender from name
Predict origin on Namsor
Predict country of origin from name
Connect Namsor to LlamaIndex via MCP
Follow these steps to wire Namsor into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Namsor MCP Server
LlamaIndex provides unique advantages when paired with Namsor through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Namsor tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Namsor tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Namsor, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Namsor tools were called, what data was returned, and how it influenced the final answer
Namsor + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Namsor MCP Server delivers measurable value.
Hybrid search: combine Namsor real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Namsor 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 Namsor for fresh data
Analytical workflows: chain Namsor queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Namsor in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Namsor immediately.
"Predict the gender for the name 'Jean Dupont'."
"What is the likely country of origin for the name Yuki Tanaka?"
"Parse the full name Dr. Maria Elena Rodriguez-Garcia into its components."
Troubleshooting Namsor MCP Server with LlamaIndex
Common issues when connecting Namsor to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNamsor + LlamaIndex FAQ
Common questions about integrating Namsor 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?
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