Mapbox Geospatial Operations Skill

Expert guidance for AI assistants on choosing the right geospatial tools from the Mapbox MCP Server. Focuses on selecting tools based on what the problem requires - geometric calculations vs routing, straight-line vs road network, and accuracy needs.

Core Principle: Problem Type Determines Tool Choice

The Mapbox MCP Server provides two categories of geospatial tools:

1. Offline Geometric Tools - Use Turf.js for pure geometric/spatial calculations
2. Routing & Navigation APIs - Use Mapbox APIs when you need real-world routing, traffic, or travel times

The key question: What does the problem actually require?

Decision Framework

Problem Characteristic	Tool Category	Why
Straight-line distance (as the crow flies)	Offline geometric	Accurate for geometric distance
Road/path distance (as the crow drives)	Routing API	Only routing APIs know road networks
Travel time	Routing API	Requires routing with speed/traffic data
Point containment (is X inside Y?)	Offline geometric	Pure geometric operation
Geographic shapes (buffers, centroids, areas)	Offline geometric	Mathematical/geometric operations
Traffic-aware routing	Routing API	Requires real-time traffic data
Route optimization (best order to visit)	Routing API	Complex routing algorithm
High-frequency checks (e.g., real-time geofencing)	Offline geometric	Instant response, no latency

Decision Matrices by Use Case

Distance Calculations

User asks: "How far is X from Y?"

What They Actually Mean	Tool Choice	Why
Straight-line distance (as the crow flies)	distance_tool	Accurate for geometric distance, instant
Driving distance (as the crow drives)	directions_tool	Only routing knows actual road distance
Walking/cycling distance (as the crow walks/bikes)	directions_tool	Need specific path network
Travel time	directions_tool or matrix_tool	Requires routing with speed data
Distance with current traffic	directions_tool (driving-traffic)	Need real-time traffic consideration

Example: "What's the distance between these 5 warehouses?"

• As the crow flies → distance_tool (10 calculations, instant)
• As the crow drives → matrix_tool (5×5 matrix, one API call, returns actual route distances)

Key insight: Use the tool that matches what "distance" means in context. Always clarify: crow flies or crow drives?

Proximity and Containment

User asks: "Which points are near/inside this area?"

Query Type	Tool Choice	Why
"Within X meters radius"	distance_tool + filter	Simple geometric radius
"Within X minutes drive"	isochrone_tool → point_in_polygon_tool	Need routing for travel-time zone, then geometric containment
"Inside this polygon"	point_in_polygon_tool	Pure geometric containment test
"Reachable by car in 30 min"	isochrone_tool	Requires routing + traffic
"Nearest to this point"	distance_tool (geometric) or matrix_tool (routed)	Depends on definition of "nearest"

Example: "Are these 200 addresses in our 30-minute delivery zone?"

1. Create zone → isochrone_tool (routing API - need travel time)
2. Check addresses → point_in_polygon_tool (geometric - 200 instant checks)

Key insight: Routing for creating travel-time zones, geometric for containment checks

Routing and Navigation

User asks: "What's the best route?"

Scenario	Tool Choice	Why
A to B directions	directions_tool	Turn-by-turn routing
Optimal order for multiple stops	optimization_tool	Solves traveling salesman problem
Clean GPS trace	map_matching_tool	Snaps to road network
Just need bearing/compass direction	bearing_tool	Simple geometric calculation
Route with traffic	directions_tool (driving-traffic)	Real-time traffic awareness
Fixed-order waypoints	directions_tool with waypoints	Routing through specific points

Example: "Navigate from hotel to airport"

• Need turn-by-turn → directions_tool
• Just need to know "it's northeast" → bearing_tool

Key insight: Routing tools for actual navigation, geometric tools for directional info

Area and Shape Operations

User asks: "Create a zone around this location"

Requirement	Tool Choice	Why
Simple circular buffer	buffer_tool	Geometric circle/radius
Travel-time zone	isochrone_tool	Based on routing network
Calculate area size	area_tool	Geometric calculation
Simplify complex boundary	simplify_tool	Geometric simplification
Find center of shape	centroid_tool	Geometric centroid

Example: "Show 5km coverage around each store"

• 5km radius → buffer_tool (geometric circles)
• "What customers can reach in 15 min?" → isochrone_tool (routing-based)

Key insight: Geometric tools for distance-based zones, routing tools for time-based zones

Performance and Scale Considerations

When Volume Affects Tool Choice

Small operations (< 100 calculations):

• Geometric tools: Instant, iterate freely
• Routing APIs: Fast enough for most uses

Medium operations (100-1,000 calculations):

• Geometric tools: Still fast, no concerns
• Routing APIs: Consider batch operations (matrix_tool)

Large operations (> 1,000 calculations):

• Geometric tools: May need optimization but still fast
• Routing APIs: Definitely use batch tools (matrix_tool handles up to 25×25)

Key insight: Volume rarely affects geometric tool choice, but routing APIs have batch tools for efficiency

Real-Time vs Batch

Use Case	Approach	Tool Choice
Real-time geofencing (every second)	Geometric checks	point_in_polygon_tool (instant)
Route planning (one-time)	Full routing	directions_tool or optimization_tool
Periodic proximity checks	Geometric distance	distance_tool
Live traffic routing	Routing with traffic	directions_tool (driving-traffic)

Architecture note for high-frequency geofencing: If the application calls containment checks at very high rates (e.g., 50 vehicles every 2 seconds = 25 checks/second), calling MCP tools over the network adds agent-reasoning overhead that makes it impractical. In those cases, recommend using Turf.js directly in-process (turf.booleanPointInPolygon) for the hot path, and reserve MCP tools for peripheral tasks like zone definition (isochrone_tool), rerouting (directions_tool), or visualization (static_map_image_tool).

Common Scenarios and Optimal Approaches

Scenario 1: Store Locator

User: "Find the closest store and show 5km coverage"

Optimal approach:

1. Search stores → category_search_tool (returns distances automatically)
2. Create coverage zone → buffer_tool (5km geometric circle)
3. Visualize → static_map_image_tool

Why: Search already gives distances; geometric buffer for simple radius

Scenario 2: Delivery Route Optimization

User: "Optimize delivery to 8 addresses / stops"

Optimal approach:

1. Geocode addresses (if needed) → Use search_and_geocode_tool to convert any street addresses to coordinates. Even when coordinates are already provided, mention this as an optional pre-step — real-world delivery lists often contain a mix of addresses and coordinates.
2. Optimize route → optimization_tool (TSP solver — reorders stops to minimize total drive time)

Why optimization_tool and NOT these alternatives:

• directions_tool only routes A → B (or through fixed-order waypoints). It does NOT reorder stops — if you pass 8 stops, it routes them in the order given, which is almost never optimal.
• matrix_tool gives travel times between all pairs of stops (8×8 = 64 values), but it does NOT compute the optimal ordering. You'd need to solve TSP yourself on top of the matrix — optimization_tool does this for you in one call.

Always mention search_and_geocode_tool as a useful companion for geocoding delivery addresses before optimization.

Scenario 3: Service Area Validation

User: "Which of these 200 addresses can we deliver to in 30 minutes?"

Optimal approach:

1. Create delivery zone → isochrone_tool (30-minute driving)
2. Check each address → point_in_polygon_tool (200 geometric checks)

Why: Routing for accurate travel-time zone, geometric for fast containment checks

Scenario 4: GPS Trace Analysis

User: "How long was this bike ride?"

Optimal approach:

1. Clean GPS trace → map_matching_tool (snap to bike paths)
2. Get distance → Use API response or calculate with distance_tool

Why: Need road/path matching; distance calculation either way works

Scenario 5: Coverage Analysis

User: "What's our total service area?"

Optimal approach:

1. Create buffers around each location → buffer_tool
2. Calculate total area → area_tool
3. Or, if time-based → isochrone_tool for each location

Why: Geometric for distance-based coverage, routing for time-based

Anti-Patterns: Using the Wrong Tool Type

❌ Don't: Use geometric tools for routing questions

// WRONG: User asks "how long to drive there?"
distance_tool({ from: A, to: B });
// Returns 10km as the crow flies, but actual drive is 15km

// CORRECT: Need routing for driving distance
directions_tool({
  coordinates: [
    { longitude: A[0], latitude: A[1] },
    { longitude: B[0], latitude: B[1] }
  ],
  routing_profile: 'mapbox/driving'
});
// Returns actual road distance and drive time as the crow drives

Why wrong: As the crow flies ≠ as the crow drives

❌ Don't: Use routing APIs for geometric operations

// WRONG: Check if point is in polygon
// (Can't do this with routing APIs)

// CORRECT: Pure geometric operation
point_in_polygon_tool({ point: location, polygon: boundary });

Why wrong: Routing APIs don't do geometric containment

❌ Don't: Confuse "near" with "reachable"

// User asks: "What's reachable in 20 minutes?"

// WRONG: 20-minute distance at average speed
distance_tool + calculate 20min * avg_speed

// CORRECT: Actual routing with road network
isochrone_tool({
  coordinates: {longitude: startLng, latitude: startLat},
  contours_minutes: [20],
  profile: "mapbox/driving"
})

Why wrong: Roads aren't straight lines; traffic varies

❌ Don't: Use routing when bearing is sufficient

// User asks: "Which direction is the airport?"

// OVERCOMPLICATED: Full routing
directions_tool({
  coordinates: [
    { longitude: hotel[0], latitude: hotel[1] },
    { longitude: airport[0], latitude: airport[1] }
  ]
});

// BETTER: Just need bearing
bearing_tool({ from: hotel, to: airport });
// Returns: "Northeast (45°)"

Why better: Simpler, instant, answers the actual question

Hybrid Approaches: Combining Tool Types

Some problems benefit from using both geometric and routing tools:

Pattern 1: Routing + Geometric Filter

1. directions_tool → Get route geometry
2. buffer_tool → Create corridor around route
3. category_search_tool → Find POIs in corridor
4. point_in_polygon_tool → Filter to those actually along route

Use case: "Find gas stations along my route"

Pattern 2: Routing + Distance Calculation

1. category_search_tool → Find 10 nearby locations
2. distance_tool → Calculate straight-line distances (geometric)
3. For top 3, use directions_tool → Get actual driving time

Use case: Quickly narrow down, then get precise routing for finalists

Pattern 3: Isochrone + Containment

1. isochrone_tool → Create travel-time zone (routing)
2. point_in_polygon_tool → Check hundreds of addresses (geometric)

Use case: "Which customers are in our delivery zone?"

Decision Algorithm

When user asks a geospatial question:

1. Does it require routing, roads, or travel times?