Placer.ai MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Placer.ai as an MCP tool provider through the 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 Placer.ai. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Placer.ai?"
)
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 Placer.ai MCP Server
Connect your AI agents to Placer.ai, the leading location intelligence platform. This MCP provides 10 tools to retrieve accurate foot traffic analytics, visitor demographics, and market rankings for millions of locations.
LlamaIndex agents combine Placer.ai tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Visitation Metrics — Retrieve estimated visits and trends for specific venues and brands with historical context
- Demographic Profiles — Understand visitor characteristics, including population estimates and trade area data
- Competitive Benchmarking — Access location rankings to compare performance against industry peers and category leaders
- Trade Area Analysis — Identify the True Trade Area (TTA) polygon for any point of interest to see where visitors come from
The Placer.ai MCP Server exposes 10 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 Placer.ai to LlamaIndex via MCP
Follow these steps to integrate the Placer.ai 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 10 tools from Placer.ai
Why Use LlamaIndex with the Placer.ai MCP Server
LlamaIndex provides unique advantages when paired with Placer.ai through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Placer.ai tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Placer.ai tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Placer.ai, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Placer.ai tools were called, what data was returned, and how it influenced the final answer
Placer.ai + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Placer.ai MCP Server delivers measurable value.
Hybrid search: combine Placer.ai real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Placer.ai 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 Placer.ai for fresh data
Analytical workflows: chain Placer.ai queries with LlamaIndex's data connectors to build multi-source analytical reports
Placer.ai MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Placer.ai to LlamaIndex via MCP:
get_api_status
Check Placer.ai API operational status
get_demographics
Get visitor demographics estimates
get_poi_details
Get complete details for a specific POI
get_rankings
Get location performance rankings
get_same_store_visits
Retrieve same-store foot traffic metrics
get_trade_area
Get True Trade Area (TTA) coordinates
get_trends
Get visit trends over time
get_visits
Retrieve foot traffic visit counts
list_properties
ai account. List properties associated with your account
search_poi
Search for specific locations or brands
Example Prompts for Placer.ai in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Placer.ai immediately.
"Get the foot traffic trends for POI ID 'poi_123' for the last month."
"Search Placer.ai for 'Walmart' locations in Miami and show their IDs."
"What is the demographic profile for the visitors of POI 'poi_abc'?"
Troubleshooting Placer.ai MCP Server with LlamaIndex
Common issues when connecting Placer.ai to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPlacer.ai + LlamaIndex FAQ
Common questions about integrating Placer.ai 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 Placer.ai 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 Placer.ai to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.