Tessie MCP Extension

Instructions

Implementation Reference

• src/index.ts:250-339 (registration)

  Registration of the 'get_weekly_mileage' MCP tool, including description, input schema, and complete handler implementation.
  // Register get_weekly_mileage tool server.tool( "get_weekly_mileage", "Calculate total miles driven in a specific week or time period", { vin: z.string().describe("Vehicle identification number (VIN)"), start_date: z.string().describe("Start date of the period (ISO format)"), end_date: z.string().describe("End date of the period (ISO format)") }, async ({ vin, start_date, end_date }) => { try { const drives = await tessieClient.getDrives(vin, start_date, end_date, 500); const totalMiles = drives.reduce((sum, drive) => sum + drive.odometer_distance, 0); // Use DriveAnalyzer to predict autopilot usage for each drive let totalAutopilotMiles = 0; const dailyStats: { [key: string]: { miles: number; drives: number; autopilot_miles: number } } = {}; drives.forEach(drive => { const date = new Date(drive.started_at * 1000).toISOString().split('T')[0]; if (!dailyStats[date]) { dailyStats[date] = { miles: 0, drives: 0, autopilot_miles: 0 }; } // Create a temporary merged drive to predict autopilot usage const tempMergedDrive = { id: `temp_${drive.id}`, originalDriveIds: [drive.id], started_at: drive.started_at, ended_at: drive.ended_at, starting_location: drive.starting_location, ending_location: drive.ending_location, starting_battery: drive.starting_battery, ending_battery: drive.ending_battery, total_distance: drive.odometer_distance, total_duration_minutes: (drive.ended_at - drive.started_at) / 60, driving_duration_minutes: (drive.ended_at - drive.started_at) / 60, stops: [], autopilot_distance: 0, // Will be predicted below autopilot_percentage: 0, energy_consumed: drive.starting_battery - drive.ending_battery, average_speed: drive.average_speed || 0, max_speed: drive.max_speed || 0 }; // Predict autopilot usage for this drive const predictedAutopilotMiles = driveAnalyzer.predictAutopilotUsage(tempMergedDrive); dailyStats[date].miles += drive.odometer_distance; dailyStats[date].drives += 1; dailyStats[date].autopilot_miles += predictedAutopilotMiles; totalAutopilotMiles += predictedAutopilotMiles; }); const breakdown = Object.entries(dailyStats).map(([date, stats]) => ({ date, miles: Math.round(stats.miles * 100) / 100, drives: stats.drives, autopilot_miles: Math.round(stats.autopilot_miles * 100) / 100, fsd_percentage: stats.miles > 0 ? Math.round((stats.autopilot_miles / stats.miles) * 10000) / 100 : 0, })); const result = { vehicle_vin: vin, period: { start_date, end_date }, summary: { total_miles: Math.round(totalMiles * 100) / 100, total_drives: drives.length, total_autopilot_miles: Math.round(totalAutopilotMiles * 100) / 100, fsd_percentage: totalMiles > 0 ? Math.round((totalAutopilotMiles / totalMiles) * 10000) / 100 : 0, }, daily_breakdown: breakdown.sort((a, b) => a.date.localeCompare(b.date)) }; // Wrap in MCP format return { content: [ { type: "text", text: JSON.stringify(result, null, 2) } ] }; } catch (error) { throw new Error(`Failed to get weekly mileage: ${error}`); } } );
• src/index.ts:259-338 (handler)

  The core handler function that implements the tool logic: fetches driving data from Tessie API, computes total and daily mileage, predicts autopilot usage via DriveAnalyzer helper, and formats MCP-compliant response.
  async ({ vin, start_date, end_date }) => { try { const drives = await tessieClient.getDrives(vin, start_date, end_date, 500); const totalMiles = drives.reduce((sum, drive) => sum + drive.odometer_distance, 0); // Use DriveAnalyzer to predict autopilot usage for each drive let totalAutopilotMiles = 0; const dailyStats: { [key: string]: { miles: number; drives: number; autopilot_miles: number } } = {}; drives.forEach(drive => { const date = new Date(drive.started_at * 1000).toISOString().split('T')[0]; if (!dailyStats[date]) { dailyStats[date] = { miles: 0, drives: 0, autopilot_miles: 0 }; } // Create a temporary merged drive to predict autopilot usage const tempMergedDrive = { id: `temp_${drive.id}`, originalDriveIds: [drive.id], started_at: drive.started_at, ended_at: drive.ended_at, starting_location: drive.starting_location, ending_location: drive.ending_location, starting_battery: drive.starting_battery, ending_battery: drive.ending_battery, total_distance: drive.odometer_distance, total_duration_minutes: (drive.ended_at - drive.started_at) / 60, driving_duration_minutes: (drive.ended_at - drive.started_at) / 60, stops: [], autopilot_distance: 0, // Will be predicted below autopilot_percentage: 0, energy_consumed: drive.starting_battery - drive.ending_battery, average_speed: drive.average_speed || 0, max_speed: drive.max_speed || 0 }; // Predict autopilot usage for this drive const predictedAutopilotMiles = driveAnalyzer.predictAutopilotUsage(tempMergedDrive); dailyStats[date].miles += drive.odometer_distance; dailyStats[date].drives += 1; dailyStats[date].autopilot_miles += predictedAutopilotMiles; totalAutopilotMiles += predictedAutopilotMiles; }); const breakdown = Object.entries(dailyStats).map(([date, stats]) => ({ date, miles: Math.round(stats.miles * 100) / 100, drives: stats.drives, autopilot_miles: Math.round(stats.autopilot_miles * 100) / 100, fsd_percentage: stats.miles > 0 ? Math.round((stats.autopilot_miles / stats.miles) * 10000) / 100 : 0, })); const result = { vehicle_vin: vin, period: { start_date, end_date }, summary: { total_miles: Math.round(totalMiles * 100) / 100, total_drives: drives.length, total_autopilot_miles: Math.round(totalAutopilotMiles * 100) / 100, fsd_percentage: totalMiles > 0 ? Math.round((totalAutopilotMiles / totalMiles) * 10000) / 100 : 0, }, daily_breakdown: breakdown.sort((a, b) => a.date.localeCompare(b.date)) }; // Wrap in MCP format return { content: [ { type: "text", text: JSON.stringify(result, null, 2) } ] }; } catch (error) { throw new Error(`Failed to get weekly mileage: ${error}`); } }
• src/index.ts:254-258 (schema)

  Input schema using Zod for parameter validation: requires VIN and date range.
  { vin: z.string().describe("Vehicle identification number (VIN)"), start_date: z.string().describe("Start date of the period (ISO format)"), end_date: z.string().describe("End date of the period (ISO format)") },
• src/drive-analyzer.ts:233-301 (helper)

  Helper method in DriveAnalyzer class that predicts FSD/autopilot mileage for individual drives, called for each drive in the weekly aggregation to estimate daily autopilot usage.
  predictAutopilotUsage(drive: MergedDrive): number { if (drive.total_distance < 2) { // Very short drives unlikely to use autopilot return 0; } let autopilotLikelihood = 0; // Factor 1: Highway speed indicator (adjusted for real-world highway averages) if (drive.average_speed > 35) { autopilotLikelihood += 0.5; // Strong highway indicator } else if (drive.average_speed > 25) { autopilotLikelihood += 0.3; // Moderate highway/arterial indicator } else if (drive.average_speed > 15) { autopilotLikelihood += 0.1; // Light highway segments } // Factor 2: Distance-based likelihood (more realistic for FSD usage patterns) if (drive.total_distance > 15) { autopilotLikelihood += 0.4; // Long drives - very likely FSD } else if (drive.total_distance > 8) { autopilotLikelihood += 0.3; // Medium drives - likely FSD } else if (drive.total_distance > 4) { autopilotLikelihood += 0.2; // Short highway segments } // Factor 3: Max speed indicator (shows highway capability) if (drive.max_speed > 65) { autopilotLikelihood += 0.2; // Highway speeds } else if (drive.max_speed > 45) { autopilotLikelihood += 0.1; // Arterial speeds } // Factor 4: Speed consistency (autopilot tends to maintain steady speeds) const speedConsistency = this.calculateSpeedConsistency(drive); autopilotLikelihood += speedConsistency * 0.15; // Factor 5: Duration factor (longer drives more likely to use autopilot) if (drive.driving_duration_minutes > 20) { autopilotLikelihood += 0.15; // Extended driving } else if (drive.driving_duration_minutes > 10) { autopilotLikelihood += 0.1; // Medium duration } // Cap likelihood and apply more realistic highway assumptions autopilotLikelihood = Math.min(autopilotLikelihood, 1.0); // Boost likelihood for clear highway patterns but keep realistic if (drive.average_speed > 40 && drive.total_distance > 20) { autopilotLikelihood = Math.max(autopilotLikelihood, 0.75); // Clear highway drive } else if (drive.average_speed > 30 && drive.total_distance > 10) { autopilotLikelihood = Math.max(autopilotLikelihood, 0.65); // Likely highway drive } // Apply realistic deductions for non-FSD portions if (drive.total_distance > 5) { // Deduct estimated parking/city driving at start and end (~1-2 miles total) const parkingDeduction = Math.min(2.0 / drive.total_distance, 0.15); autopilotLikelihood = Math.max(0, autopilotLikelihood - parkingDeduction); } // Cap maximum realistic FSD usage (even perfect highway drives have some manual portions) autopilotLikelihood = Math.min(autopilotLikelihood, 0.92); // Max 92% FSD usage // Calculate estimated autopilot miles const estimatedAutopilotMiles = drive.total_distance * autopilotLikelihood; return Math.round(estimatedAutopilotMiles * 100) / 100; }