Foursquare 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 Foursquare 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 Foursquare. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Foursquare?"
)
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 Foursquare MCP Server
Connect your Foursquare account to any AI agent and take full control of your geospatial intelligence and place discovery workflows through natural conversation.
LlamaIndex agents combine Foursquare tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Place Discovery Orchestration — Identify bounded routing spaces inside the headless Foursquare POI graph and extract explicitly attached REST arrays targeting specific search queries near any GPS pin
- Rich Metadata Inspection — Perform structural extraction of properties driving active node schemas, retrieving mega-document payloads including hours, ratings, and precise mapping arrays natively
- Visual & Social Auditing — Retrieve explicit cloud logging tracing media URL limits to compile dynamic image URLs and capture raw text sentiments left by humans to track venue quality
- Geospatial Intelligence — Execute immediate queries within custom drawn multi-point geometries or specific radius boundaries to find what exists physically adjacent to any target
- Precise Venue Matching — Dispatch automated validation checks routing explicit duplication logic to force Foursquare to confidently return exactly one node for ambiguous strings
- Intelligent Autocomplete — Provision highly-available JSON payloads generating fast typeaheads by querying partial letters to predict user intent natively
- Taxonomy Oversight — Enumerate explicitly attached structured rules exporting the entire official Foursquare classification tree to resolve internal type codes flawlessly
The Foursquare 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 Foursquare to LlamaIndex via MCP
Follow these steps to integrate the Foursquare 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 Foursquare
Why Use LlamaIndex with the Foursquare MCP Server
LlamaIndex provides unique advantages when paired with Foursquare through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Foursquare tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Foursquare tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Foursquare, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Foursquare tools were called, what data was returned, and how it influenced the final answer
Foursquare + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Foursquare MCP Server delivers measurable value.
Hybrid search: combine Foursquare real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Foursquare 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 Foursquare for fresh data
Analytical workflows: chain Foursquare queries with LlamaIndex's data connectors to build multi-source analytical reports
Foursquare MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Foursquare to LlamaIndex via MCP:
autocomplete_venues
Provision a highly-available JSON Payload generating fast typeaheads
get_place_details
Perform structural extraction of properties driving active Node schemas
get_place_photos
Retrieve explicit Cloud logging tracing explicit Media URL limits
get_place_tips
Identify precise active arrays spanning native User Reviews
list_venue_categories
Enumerate explicitly attached structured rules exporting active Taxonomy
match_venue_exactly
Dispatch an automated validation check routing explicit Duplication logic
search_nearby_venues
Inspect deep internal arrays mitigating specific Radius targets
search_places
Identify bounded routing spaces inside the Headless Foursquare POI graph
search_within_polygon
Retrieve the exact structural matching verifying Geofence alternatives
search_within_radius
Irreversibly vaporize explicit validations extracting rich schema scopes
Example Prompts for Foursquare in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Foursquare immediately.
"Find coffee shops near '40.71, -74.00'"
"What are the opening hours for 'Central Park Zoo'?"
"Show me user tips for 'The Met Museum'"
Troubleshooting Foursquare MCP Server with LlamaIndex
Common issues when connecting Foursquare to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFoursquare + LlamaIndex FAQ
Common questions about integrating Foursquare 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 Foursquare 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 Foursquare to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.