Retrieve comprehensive details for a Foursquare place by supplying its unique FSQ ID.
MIT
215,493 tools. Last updated 2026-06-20 00:24
"Foursquare" matching MCP tools:
- Retrieve user-generated tips and reviews for a specific Foursquare venue using its unique identifier. Useful for gathering local insights and recommendations.MIT
- Autocomplete Foursquare place search queries to quickly find locations and venues.MIT
- Retrieve photos for a Foursquare venue by providing its FSQ ID.MIT
- Search for places on Foursquare by providing a query, location, or coordinates. Returns matching venues with details.MIT
- 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
- Retrieve your most visited venues with visit counts from Foursquare Swarm check-in history. Filter by category, city, state, or country.MIT
- Retrieve statistical summary of your check-in history, including total count and date range. Useful for analyzing your Foursquare Swarm activity.MIT
- Fetch full details of a Foursquare Swarm check-in by providing its check-in ID.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
- Retrieve your Foursquare Swarm check-in history with venue details, timestamps, and photos. Paginate and sort results to find past check-ins.MIT
- Get recent Foursquare Swarm check-ins with configurable lookback period and result limit.MIT
- Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.Connector
- Keyless POI / business directory search - the no-API-key, no-signup, pay-per-query alternative to Google Places / Foursquare / Yelp. Pass an area (?area=Soho, New York, or ?bbox=south,west,north,east) and a category (?category=food|cafe|restaurant|bar|retail|grocery|hotel|health|pharmacy|finance|bank|fuel|automotive|education|gym) or raw OSM tag (?tag=shop=bakery), and get ONE structured JSON list of matching businesses - each with name, category, full address, phone, website, opening_hours, brand and lat/lon. For lead-generation, local-business intelligence, retail/CPG distribution mapping, logistics and competitive-mapping agents. Source: OpenStreetMap Overpass + Nominatim (ODbL), keyless. Business/place/POI public commercial listings only, no people, no PII. $0.01 USDC on Base via x402. Coverage varies by region; not a verified business registry. [x402 paid tool: GET /api/x402/poi-business-search-json?src=mcp returns the 402 challenge with the canonical payTo; price 0.01 USDC on Base eip155:8453.]Connector
- Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.Connector
- Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,777 tools across 896 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.Connector
- REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.Connector
- "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.Connector
- Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.Connector
- Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 3,777 across 896 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.Connector