Foursquare MCP. Find POIs, check details, and map venues using chat.
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Foursquare. Get deep, structured location intelligence directly in your chat. Search millions of venues, pull precise metadata like hours and ratings, and analyze POI density using geometry.
You can find exactly what you need—from the best coffee shop near a landmark to checking service hours within a specific polygon.
What your AI agents can do
Autocomplete venues
Provides quick, predictive suggestions as you type venue names.
Get place details
Extracts detailed structured properties, including hours and ratings, for a specific venue.
Get place photos
Retrieves dynamic URLs for images associated with a venue.
Find venues using coordinates, or draw search boundaries using polygons and specific radii.
Retrieve structured properties like operating hours, ratings, and category information for any identified place.
Get user-submitted tips and raw text reviews to gauge community sentiment about a location.
Compile dynamic image URLs by retrieving cloud logs that trace media availability for a venue.
Get fast, predictive suggestions by querying partial letters for venue names.
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Foursquare MCP Server: 10 Tools for Location Data
These tools allow you to perform deep, complex location searches, extract structured details, and analyze POI data using the Foursquare API.
019d759eautocomplete venues
Provides quick, predictive suggestions as you type venue names.
019d759eget place details
Extracts detailed structured properties, including hours and ratings, for a specific venue.
019d759eget place photos
Retrieves dynamic URLs for images associated with a venue.
019d759eget place tips
Collects and structures user-submitted reviews and tips for a venue.
019d759elist venue categories
Exports the full Foursquare classification tree to understand all possible venue types.
019d759ematch venue exactly
Forces the system to return one specific node for an ambiguous venue name, preventing multiple results.
019d759esearch nearby venues
Searches for venues within a specific radius of a given location.
019d759esearch places
Performs a general search for points of interest within the Foursquare graph.
019d759esearch within polygon
Finds venues that fall inside a precisely defined geographic boundary.
019d759esearch within radius
Executes a search, extracting rich schemas scoped by a specific radius.
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What you can do with this MCP connector
You can use your AI client to find deep, structured location intelligence straight from Foursquare. You don't gotta click through web forms; you just ask.
Search by Location: You can run general searches using search_places, or narrow things down with search_nearby_venues by giving coordinates. You can also find venues that sit inside a precise shape using search_within_polygon, or run a search limited by a specific distance using search_within_radius.
Predict Search Terms: When you start typing a venue name, autocomplete_venues gives you quick, predictive suggestions. If you know the exact name, match_venue_exactly forces the system to return only one result, avoiding any ambiguity.
Extract Venue Details: Once you find a place, get_place_details pulls structured data like operating hours and star ratings. You can also pull all the associated images with get_place_photos, and get user-submitted tips and raw reviews using get_place_tips.
Classification & Data: You can see every type of venue out there by running list_venue_categories, which exports the full Foursquare classification tree. You can also perform a general search for points of interest using search_places.
How Foursquare MCP Works
- 1 First, subscribe to the server and provide your Foursquare API Key (find this in the Foursquare Developer Console).
- 2 Next, tell your AI agent exactly what you need—for example, 'Find all Italian restaurants within 500 meters of 34.05, -118.25'.
- 3 The agent executes the appropriate tool (like
search_nearby_venues), and you receive structured data (JSON/text) with the results.
The bottom line is, you talk to your agent, and it runs the complex location queries and returns clean, structured data.
Who Is Foursquare MCP For?
This is for anyone who relies on location data but hates manual web scraping. Think urban planners, field researchers, or app developers building location services. If your job involves validating POI density or checking competitor hours across multiple sites, this saves hours of clicking and copy-pasting.
Audits the density and quality of Points of Interest (POIs) within specific geographic polygons or defined neighborhoods.
Checks specific venue operating hours and finds nearby points of interest without opening a browser.
Tests location-based search parameters and validates venue schemas by talking to the agent.
What Changes When You Connect
- Get structured data instantly. Instead of wading through web pages, tools like
get_place_detailspull clean fields (hours, ratings) directly to your conversation window. - Pinpoint exact locations. Use
search_within_polygonto analyze density in a specific city block or neighborhood, something simple searches can't do. - Gauge real-world sentiment.
get_place_tipspulls user reviews and raw text, giving you a better sense of quality than just the star rating. - Never guess a venue name again.
match_venue_exactlyresolves ambiguity, making sure your search query hits the precise target node. - Understand the taxonomy.
list_venue_categoriesgives you the entire Foursquare classification tree, which is critical for building custom filtering logic. - Automate asset gathering.
get_place_photoscompiles dynamic image URLs, letting you audit the visual presence of a venue without manual photo searches.
Real-World Use Cases
Checking competitor hours in a new market.
A market researcher needs to know the operating hours for five competing retail stores. Instead of visiting each store's website, they ask their agent to run search_nearby_venues and then loop through the results, calling get_place_details for each one. The agent compiles a clean list of opening hours in minutes.
Validating a new development site.
A real estate analyst needs to know if a new development area has sufficient foot traffic potential. They define a custom polygon and run search_within_polygon. This tool returns a count and list of POIs, letting the analyst confirm density and commercial viability.
Investigating a specific historical landmark.
A historian needs to find all associated events and media. They ask the agent to run search_places near the landmark's coordinates, then use get_place_photos and get_place_tips to collect visual evidence and raw visitor sentiment.
Debugging a location search feature.
An app developer needs to make sure their app only returns one result even if the user types a vague name. They use match_venue_exactly to validate the function, confirming the system handles duplication logic and returns a single, confident node.
The Tradeoffs
Asking for all details in one go
Asking 'Give me all details about this place' often results in a massive, unreadable wall of text, mixing hours, tips, and photos into one dump.
→
Break it down. First, use get_place_details for hours and ratings. Then, run get_place_tips separately to get reviews. Finally, use get_place_photos to compile the image URLs. This gives you actionable, separated data.
Assuming the search is broad enough
Using search_places when you actually need results inside a specific boundary (e.g., a campus or a city block). The results might include things too far out.
→
Always define your scope. If you need a contained area, use search_within_polygon or search_within_radius instead of the general search. This keeps the results tight and relevant.
Ignoring name ambiguity
Running a general search on a common name like 'Starbucks' might return hundreds of nodes, forcing you to manually pick the right one.
→
If you suspect ambiguity, run match_venue_exactly first. It forces the system to validate and return only one node, giving you a single, confirmed target.
When It Fits, When It Doesn't
Use this server if your job requires structured, multi-stage location intelligence. Specifically, if you need to combine searching (e.g., search_nearby_venues) with deep data extraction (e.g., get_place_details, get_place_tips). Don't use it if you just need a simple list of search results; use a basic mapping API for that. If your goal is to build a functional layer that needs to validate data structures or understand the entire classification system, then list_venue_categories is essential. However, if you are only doing one simple task—like just getting a list of names—you might over-engineer the solution; stick to the specific tool for that single job.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Foursquare. 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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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually checking venue hours and POI density is a massive time sink.
Today, figuring out the operational hours for a cluster of competitor locations means opening a dozen browser tabs. You copy the address, navigate to the site, scroll past the menu, and manually find the 'Hours' section. If you need to check density, you're bouncing between Google Maps, Yelp, and dedicated city planning sites, spending minutes just copying coordinates.
With the Foursquare MCP Server, you just ask. Tell your agent, 'What are the hours and density of POIs within this polygon?' The system handles the complex data aggregation, running targeted queries and returning a clean, structured list of facts in seconds.
Foursquare MCP Server: Get rich, structured location data.
Before this server, collecting rich data required multiple, disparate calls: one to find the place, another to pull photos, and a third to get user reviews. This process was slow and often broke when one service timed out.
Now, you chain the calls. You find the place with `search_places`, then pull photos with `get_place_photos`, and finally gather sentiment with `get_place_tips`. It's a reliable, conversational pipeline that makes the data work for you.
Common Questions About Foursquare MCP
How do I use the `search_within_polygon` tool? +
You provide the coordinates defining the polygon boundary. This tool then finds all POIs that fall entirely inside that specific, drawn shape, giving you a highly contained set of results.
Does `get_place_details` include opening hours? +
Yes, get_place_details is designed to extract structural properties, and operating hours are one of the key fields it retrieves, giving you a clean date/time payload.
What's the difference between `search_places` and `search_nearby_venues`? +
search_places performs a general search across the entire Foursquare graph. search_nearby_venues is more focused, designed to look for venues within a specific radius of a given coordinate.
How can I check user sentiment using `get_place_tips`? +
You run get_place_tips on a venue ID. It collects raw text from user reviews, allowing you to analyze sentiment beyond just the star rating.
How do I use `match_venue_exactly` to resolve ambiguous location names? +
The match_venue_exactly tool validates location strings, ensuring Foursquare returns only one node even if the name is ambiguous. It routes explicit duplication logic, giving you a single, confident result instead of multiple options.
What is the purpose of `autocomplete_venues`? +
autocomplete_venues provides fast typeaheads by querying partial letters, predicting user intent before the full search. It returns a highly-available JSON payload, speeding up the initial user input phase.
Can I get photo URLs using `get_place_photos`? +
Yes, get_place_photos retrieves explicit cloud logging tracing media URL limits. This means you get the direct image links needed to display visual content for any discovered venue.
How does `list_venue_categories` help with taxonomy? +
The list_venue_categories tool enumerates the structured rules of the Foursquare classification tree. You can export the entire official taxonomy, which helps resolve internal type codes flawlessly in your applications.
Can my agent search for places within a specific drawn area (polygon)? +
Yes. Use the 'search_within_polygon' tool. Provide a coordinate string representing the multi-point geometry. The agent will analyze the localized boundaries and restrict the POI lookup strictly inside that drawn area.
How do I retrieve the opening hours and ratings for a venue via chat? +
Use the 'get_place_details' tool. Provide the Foursquare ID (fsq_id). The agent will fetch the full payload including operating hours, average ratings, and price levels natively from the places graph.
Can I see photos and user tips for a specific store through the agent? +
Absolutely. Use the 'get_place_photos' and 'get_place_tips' tools. Your agent will pull dynamic image URLs and capture raw text sentiments left by other visitors to help you evaluate venue quality flawlessly.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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