Mapbox Search Patterns Skill

Expert guidance for AI assistants on using Mapbox search tools effectively. Covers tool selection, parameter optimization, and best practices for geocoding, POI search, and location discovery.

Available Search Tools

1. search_and_geocode_tool

Best for: Specific places, addresses, brands, named locations

Use when query contains:

• Specific names: "Starbucks on 5th Avenue", "Empire State Building"
• Brand names: "McDonald's", "Whole Foods"
• Addresses: "123 Main Street, Seattle", "1 Times Square"
• Chain stores: "Target"
• Cities/places: "San Francisco", "Portland"

Don't use for: Generic categories ("coffee shops", "museums")

2. category_search_tool

Best for: Generic place types, categories, plural queries

Use when query contains:

• Generic types: "coffee shops", "restaurants", "gas stations"
• Plural forms: "museums", "hotels", "parks"
• Is-a phrases: "any coffee shop", "all restaurants", "nearby pharmacies"
• Industry terms: "electric vehicle chargers", "ATMs"

Don't use for: Specific names or brands

3. reverse_geocode_tool

Best for: Converting coordinates to addresses, cities, towns, postcodes

Use when:

• Have GPS coordinates, need human-readable address
• Need to identify what's at a specific location
• Converting user location to address

Tool Selection Decision Matrix

User Query	Tool	Reasoning
"Find Starbucks on Main Street"	search_and_geocode_tool	Specific brand name
"Find coffee shops nearby"	category_search_tool	Generic category, plural
"What's at 37.7749, -122.4194?"	reverse_geocode_tool	Coordinates to address
"Empire State Building"	search_and_geocode_tool	Specific named POI
"hotels in downtown Seattle"	category_search_tool	Generic type + location
"Target store locations"	search_and_geocode_tool	Brand name (even plural)
"any restaurant near me"	category_search_tool	Generic + "any" phrase
"123 Main St, Boston, MA"	search_and_geocode_tool	Specific address
"electric vehicle chargers"	category_search_tool	Industry category
"McDonald's"	search_and_geocode_tool	Brand name

Parameter Guidance

Proximity vs Bbox vs Country

Three ways to spatially constrain search results:

1. proximity (STRONGLY RECOMMENDED)

What it does: Biases results toward a location, but doesn't exclude distant matches

Use when:

• User says "near me", "nearby", "close to"
• Have a reference point but want some flexibility
• Want results sorted by relevance to a point

Example:

{
  "q": "pizza",
  "proximity": {
    "longitude": -122.4194,
    "latitude": 37.7749
  }
}

Why this works: API returns SF pizza places first, but might include famous NYC pizzerias if highly relevant

Critical: Always set proximity when you have a reference location! Without it, results are IP-based or global.

2. bbox (Bounding Box)

What it does: Hard constraint - ONLY returns results within the box

Use when:

• User specifies an area: "in downtown", "within this neighborhood"
• Have a defined service area
• Need to guarantee results are within bounds

Example:

{
  "q": "hotel",
  "bbox": [-122.51, 37.7, -122.35, 37.83] // [minLon, minLat, maxLon, maxLat]
}

Why this works: Guarantees all hotels are within SF's downtown area

Watch out: Too small = no results; too large = irrelevant results

3. country

What it does: Limits results to specific countries

Use when:

• User specifies country: "restaurants in France"
• Building country-specific features
• Need to respect regional boundaries
• Or it is otherwise clear they want results within a specific country

Example:

{
  "q": "Paris",
  "country": ["FR"] // ISO 3166 alpha-2 codes
}

Why this works: Finds Paris, France (not Paris, Texas)

Can combine: proximity + country + bbox or any combination of the three

Decision Matrix: Spatial Filters

Scenario	Use	Why
"Find coffee near me"	proximity	Bias toward user location
"Coffee shops in downtown Seattle"	proximity + bbox	Center on downtown, limit to area
"Hotels in France"	country	Hard country boundary
"Best pizza in San Francisco"	proximity + country ["US"]	Bias to SF, limit to US
"Gas stations along this route"	bbox around route	Hard constraint to route corridor
"Restaurants within 5 miles"	proximity (then filter by distance)	Bias nearby, filter results

Setting limit Parameter

category_search_tool only (1-25, default 10)

Use Case	Limit	Reasoning
Quick suggestions	5	Fast, focused results
Standard list	10	Default, good balance
Comprehensive search	25	Maximum allowed
Map visualization	25	Show all nearby options
Dropdown/autocomplete	5	Don't overwhelm UI

Performance tip: Lower limits = faster responses

types Parameter (search_and_geocode_tool)

Filter by feature type:

Type	What It Includes	Use When
poi	Points of interest (businesses, landmarks)	Looking for POIs, not addresses
address	Street addresses	Need specific address
place	Cities, neighborhoods, regions	Looking for area/region
street	Street names without numbers	Need street, not specific address
postcode	Postal codes	Searching by ZIP/postal code
district	Districts, neighborhoods	Area-based search
locality	Towns, villages	Municipality search
country	Country names	Country-level search

Example combinations:

// Only POIs and addresses, no cities
{"q": "Paris", "types": ["poi", "address"]}
// Returns Paris Hotel, Paris Street, not Paris, France

// Only places (cities)
{"q": "Paris", "types": ["place"]}
// Returns Paris, France; Paris, Texas; etc.

Default behavior: All types included (usually what you want)

auto_complete Parameter (search_and_geocode_tool)

What it does: Enables partial/fuzzy matching

Setting	Behavior	Use When
true	Matches partial words, typos	User typing in real-time
false (default)	Exact matching	Final query, not autocomplete

Example:

// User types "starb"
{ "q": "starb", "auto_complete": true }
// Returns: Starbucks, Starboard Tavern, etc.

Use for:

• Search-as-you-type interfaces
• Handling typos ("mcdonalds" -> McDonald's)

• Incomplete queries

Don't use for:

• Final/submitted queries (less precise)
• When you need exact matches

Anti-Patterns to Avoid

Don't: Use category_search for brands

// BAD
category_search_tool({ category: 'starbucks' });
// "starbucks" is not a category, returns error

// GOOD
search_and_geocode_tool({ q: 'Starbucks' });

Don't: Use search_and_geocode for generic categories

// BAD
search_and_geocode_tool({ q: 'coffee shops' });
// Less precise, may return unrelated results

// GOOD
category_search_tool({ category: 'coffee_shop' });

Don't: Forget proximity for local searches

// BAD - Results may be anywhere globally
category_search_tool({ category: 'restaurant' });

// GOOD - Biased to user location
category_search_tool({
  category: 'restaurant',
  proximity: { longitude: -122.4194, latitude: 37.7749 }
});

Don't: Use bbox when you mean proximity

// BAD - Hard boundary may exclude good nearby results
search_and_geocode_tool({
  q: 'pizza',
  bbox: [-122.42, 37.77, -122.41, 37.78] // Tiny box
});

// GOOD - Bias toward point, but flexible
search_and_geocode_tool({
  q: 'pizza',
  proximity: { longitude: -122.4194, latitude: 37.7749 }
});

Don't: Request ETA unnecessarily

// BAD - Costs API quota for routing calculations
search_and_geocode_tool({
  q: 'museums',
  eta_type: 'navigation',
  navigation_profile: 'driving'
});
// User didn't ask for travel time!

// GOOD - Only add ETA when needed
search_and_geocode_tool({ q: 'museums' });
// If user asks "how long to get there?", then add ETA

Don't: Set limit too high for UI display

// BAD - Overwhelming for simple dropdown
category_search_tool({
  category: 'restaurant',
  limit: 25
});
// Returns 25 restaurants for a 5-item dropdown

// GOOD - Match UI needs
category_search_tool({
  category
how to use mapbox-search-patterns
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use mapbox-search-patterns on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add mapbox-search-patterns
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-search-patternscopy
The skills CLI fetches mapbox-search-patterns from GitHub repository mapbox/mapbox-agent-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/mapbox-search-patterns
Reload or restart Cursor to activate mapbox-search-patterns. Access the skill through slash commands (e.g., /mapbox-search-patterns) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?