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466,360 tools. Updated 2026-08-19 13:13

"Foursquare" matching MCP tools:

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    Foursquare Places MCP server enabling AI agents to search and retrieve venue data via the 2025 Places API through the Pipeworx gateway.
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    MIT
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    Enables place search and discovery via the Foursquare Places API v3, allowing AI assistants to find venues, search near locations, look up place details, and get location context.
  • Assess commercial area saturation by counting businesses per category within a radius, showing density, ratings, and top places for market analysis.
    Apache 2.0
  • Retrieve the complete list of all Foursquare Swarm check-ins by automatically paginating through the full history. This method makes one API call per 250 check-ins, so use with caution for large datasets.
    MIT
  • Check if a place exists by cross-referencing OSM, Foursquare, and OpenTripMap. Get verification status, confidence score, and canonical name to avoid hallucinated results.
    MIT
  • Retrieve your most visited venues with visit counts from Foursquare Swarm check-in history. Filter by category, city, state, or country.
    MIT
  • Find restaurants, bars, pubs, or cafes near any location. Filter by cuisine, radius, or category and get links to Google Maps, Zomato, TripAdvisor, Yelp, OpenTable, Resy, Untappd, and Foursquare.
    MIT
  • Retrieve statistical summary of your check-in history, including total count and date range. Useful for analyzing your Foursquare Swarm activity.
    MIT
  • Retrieve your Foursquare Swarm check-in history with venue details, timestamps, and photos. Paginate and sort results to find past check-ins.
    MIT
  • Send a venue location with name, address, and coordinates to a Telegram chat. Supports Foursquare and Google Places IDs, reply markup, and message protection.
    MIT
  • Send a venue's location, title, and address to a Telegram chat, with optional Foursquare or Google Place details.
    MIT
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1462 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,564 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    Connector
  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
    Connector