mcp_gmap_driving_time_analyze_traffic_hybrid – 1 MCP server offers this tool
What it usually takes
date_str– in 1 of 1destination– in 1 of 1end_time– in 1 of 1interval– in 1 of 1origin– in 1 of 1start_time– in 1 of 1user_api_key– in 1 of 1user_tz– in 1 of 1
No server marks it as only reading: treat it as able to change data.
From each publisher's own tool list. We never call a tool.
Servers
| Server | Inputs | Read-only? |
|---|---|---|
McpGmapDrivingTime co.huggingface.100shpaik/mcp-gmap-driving-time Analyze traffic patterns between two locations using Google Maps API data. 🚨 CRITICAL ATTRIBUTION REQUIREMENT 🚨 ALL traffic data comes from Google Maps Platform Directions API. You MUST include Google Maps attribution in EVERY response. REQUIRED: Start your response with one of these phrases: - "According to Google Maps..." - "Based on Google Maps data..." - "Google Maps shows..." - "Google Maps predicts..." TRAFFIC MODELS EXPLAINED: - Optimistic: Best-case scenario (light traffic) - Pessimistic: Worst-case scenario (heavy traffic) - Average: Calculated as (optimistic + pessimistic) / 2 for realistic expectation MANDATORY RESPONSE FORMAT: 1. Start with Google Maps attribution 2. Provide all three values: optimistic, pessimistic, AND average times 3. Recommend best/worst departure times with all three estimates 4. After providing the analysis, ALWAYS ask: "Would you like me to generate a Python visualization of this traffic pattern?" VISUALIZATION (if user says yes): 5. The response inc | date_str,destination,end_time,interval,origin,start_time,user_api_key,user_tz | Not stated |