Local Discovery

Local & National Discovery

Find events, activities, venues, and things happening in the user's area (default: the user's local area) or anywhere nationally. Covers two subdomains:

1. Events — concerts, festivals, comedy, things to do tonight/this weekend
2. Venues — bars, lounges, restaurants, nightlife spots matching specific criteria (vibe, amenities, atmosphere)

When to Use

• User asks about local events, things to do tonight/this weekend
• "What's happening around here?" or similar discovery requests
• User asks for venue recommendations with specific criteria (e.g., "cigar lounge," "speakeasy," "classy bar") — local OR national
• User asks "find me X somewhere" without specifying a city — treat as national search
• Need to surface relevant activities based on user interests

Venue Discovery Workflow

When the user asks for venues rather than events, use this approach:

1. Start with Google Maps for venue discovery (PRIMARY TOOL)

Navigate directly to Google Maps search:

browser_navigate(url="https://www.google.com/maps/search/cigar+lounge+near+<city>+<state>")

Google Maps works reliably even when Google Search CAPTCHAs. Returns listings with ratings, review counts, addresses, hours, phone numbers, and user-submitted photos. Click individual venues for detailed info, reviews, and photos (use "Vibe" photo filter for atmosphere shots).

2. Fall back to web_search if needed

Search using specific criteria keywords (e.g., "cigar lounge", "speakeasy bar"). Collect names, addresses, and phone numbers from search results.

PITFALL: web_search relies on ddgs which is installed in the system Python (~/Library/Python/3.9/...) but Hermes runs from a venv that doesn't see it. If you get "ddgs package is not installed", skip to step 3 immediately — don't retry or try to reinstall. This is a persistent env path issue, not transient.

3. Skip review aggregator sites for browser navigation — they CAPTCHA aggressively

PITFALL: Yelp, TripAdvisor, DuckDuckGo, and Bing ALL serve CAPTCHAs to browser sessions. Don't waste time trying to extract reviews or listings from them via browser_navigate — go straight to venue websites.

EXCEPTION: web_extract works on TripAdvisor despite browser CAPTCHA. For national/regional venue discovery, use web_extract(urls=[tripadvisor_url]) instead of browser navigation.

4. Go directly to venue websites via browser_navigate

Venue websites are the most reliable source for accurate, current info:

• Hours — always verify from the official site, never trust Google's cached hours
• Dress code — many upscale venues post this explicitly
• Age restrictions — check before recommending
• Contact info — phone and email

5. Go beyond the algorithmic top-10 when the category is the city's identity

PITFALL: When a city is FAMOUS for a venue category (e.g. Louisville + bourbon, Nashville + music venues, NOLA + jazz clubs, Austin + BBQ, etc.), presenting just a Yelp/TripAdvisor top-10 feels insultingly thin. The user knows the city is dense in that category and expects the FULL directory — all options, organized by neighborhood.

Recovery pattern:

• Run multiple web_search queries with specific venue names and cross-streets to surface listings the algorithm may have buried
• Cross-reference Yelp snippets, TripAdvisor snippets, and niche directory sites (e.g., cigarlounges.co) from search results — even when web_extract fails on the full page, the search snippets carry review counts, ratings, and addresses
• Search for niche directory/blog articles specific to that category (e.g., "complete list of cocktail lounges", "every jazz club in New Orleans")
• Organize results by neighborhood/area — this adds massive value for the user planning a visit or crawl
• Present the full count upfront ("28 across the metro") so the user knows the list is comprehensive, not truncated

6. When the user wants bar-first / coed / date-night rather than a niche enthusiast scene

Do not keep feeding them classic category leaders if the venue photos or vibe read as male-dominated / hobbyist-only. Pivot the search intentionally:

• Reframe the target from "cigar lounge" to "restaurant or cocktail lounge with cigar patio/garden/menu".
• Search local lifestyle/tourism sources for date night, Restaurant Row, outdoor dining, nightlife, hotel lounges, and craft cocktails.
• Check Reddit (r/<metro>) for lived-experience notes like quiet, older crowd, good for couples, great restaurants around there, people watching, or bar hop after.
• Distinguish three different classes clearly:

1. Guaranteed cigar infrastructure — official site explicitly mentions cigar lounge/garden/menu/patio.
2. Bar-first with likely cigar compatibility — local/tourism sources mention cigars, smoking patio, or cigar menu, but the venue is primarily a restaurant/bar.
3. Great vibe but cigar certainty weak — good coed/date-night energy, but hookah/smoking policy or cigar policy is not verified.

• Be honest when a place is hookah-forward rather than cigar-verified.
• Prefer options where the venue identity reads mixed crowd / couples / date night over enthusiast-heavy cigar rooms when the user is going with a partner.

Useful source types for this pivot:

• official venue sites
• Visit <metro> / International Drive <metro> listings
• <metro> Date Night Guide / local lifestyle blogs
• old.reddit.com threads in r/<metro> when mainstream extractors do not support Reddit

7. Present results concisely

Format each venue with: name, address, phone, hours, vibe description, and why it fits the user's criteria. Group by neighborhood/area when the list is large. End with a clear recommendation based on their stated preferences.

Workflow

1. Try web_search first (but expect failure)

web_search(query="events tonight in my city")

PITFALL: web_search relies on ddgs which is installed in the system Python (~/Library/Python/3.9/...) but Hermes runs from a venv that doesn't see it. If you get "ddgs package is not installed", skip to step 2 immediately — don't retry or try to reinstall. This is a persistent env path issue, not transient.

0b (special case: niche-interest + multi-city search)

When the user asks for events around a specific interest (e.g., "cigar events," "car shows," "DJ night," "food festival") across multiple cities or statewide:

• Run parallel web_search calls with structured queries, e.g.:
• "cigar" event "<date>" <state>
• "cigar" tasting <city> <date>
• "cigar" event <metro> June 20-22 2026
• Use web_extract on Eventbrite's city-specific pages:
• https://www.eventbrite.com/d/<state>--<city>/<category>/
• https://www.eventbrite.com/d/<state>--<metro>/cigar/
• etc.
• Check niche vendors' event calendars (e.g., Cigars International, specialty lounges) — they regularly host tastings and live-music cigar nights that general aggregators miss.
• Present results grouped by city, then date; include only events with concrete details (date/time/location).

This avoids the "only the local metro" trap when user interest is statewide.