{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_alexcz-a11y-tessie-mcp-fixx","slug":"alexcz-a11y-tessie-mcp-fixx","name":"Tessie Insights （fork from keithah/tessie-mcp）","type":"mcp","url":"https://github.com/alexcz-a11y/tessie-mcp-fix","page_url":"https://unfragile.ai/alexcz-a11y-tessie-mcp-fixx","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:alexcz-a11y/tessie-mcp-fixx"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_0","uri":"capability://tool.use.integration.real.time.vehicle.telemetry.streaming.via.mcp.protocol","name":"real-time vehicle telemetry streaming via mcp protocol","description":"Exposes Tesla vehicle state (battery, location, climate, charge status) as MCP tools that LLM agents can call synchronously. Uses the Tessie API as a proxy to Tesla's unofficial endpoints, translating HTTP responses into structured JSON that conforms to MCP's tool-calling schema. Enables agents to query live vehicle data without polling or managing connection state themselves.","intents":["I want my AI agent to check my Tesla's current battery percentage and location in real-time","I need to build a chatbot that answers questions about my vehicle's current state","I want to trigger vehicle actions (lock, climate control) from an LLM conversation"],"best_for":["LLM application developers building Tesla-aware agents","Home automation enthusiasts integrating Tesla with AI assistants","Developers prototyping multi-vehicle fleet monitoring systems"],"limitations":["Depends on Tessie API availability and rate limits (typically 100 requests/minute per vehicle)","No local caching — every MCP tool call hits Tessie's servers, adding 200-500ms latency","Only supports vehicles linked to Tessie account; requires prior Tesla authentication outside this MCP","Real-time updates are pull-based (agent must call tools), not push-based (no webhooks)"],"requires":["Tessie API key with valid Tesla vehicle linked","MCP-compatible host (Claude Desktop, LLM frameworks supporting MCP protocol)","Network connectivity to Tessie API endpoints","Node.js 16+ (if running as standalone MCP server)"],"input_types":["MCP tool call parameters (vehicle_id, optional filters)","Structured JSON queries from LLM context"],"output_types":["JSON objects containing vehicle telemetry (battery_level, location, climate_state, charge_state)","Structured MCP tool results with typed fields"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_1","uri":"capability://data.processing.analysis.charging.cost.and.efficiency.analytics.with.time.series.aggregation","name":"charging cost and efficiency analytics with time-series aggregation","description":"Aggregates historical charging sessions from Tessie into cost-per-kWh, cost-per-mile, and efficiency trend metrics. Processes raw charge event logs (timestamp, energy added, cost, duration) into bucketed time-series data (daily, weekly, monthly) for trend analysis. Exposes analytics as MCP tools that return structured summaries, enabling agents to answer questions like 'How much did I spend on charging last month?' or 'What's my average efficiency trend?'.","intents":["I want to understand my charging costs over time and identify expensive charging sessions","I need to calculate my effective cost-per-mile to compare against gas vehicles","I want to detect anomalies in charging efficiency that might indicate battery degradation"],"best_for":["Tesla owners optimizing charging costs and energy budgets","Fleet managers tracking per-vehicle operational costs","Developers building personal finance dashboards that include vehicle costs"],"limitations":["Requires historical charging data in Tessie; limited to data available in Tessie's database (typically 6-12 months)","Aggregation is computed on-demand per MCP call, not pre-computed — complex queries may take 1-3 seconds","No support for custom time windows or granular filtering (e.g., by charger type, location); only predefined buckets","Cost calculations depend on accurate Tessie pricing data; may not reflect actual utility rates if user has variable-rate plans"],"requires":["Tessie API key with historical charging data available","MCP host with sufficient timeout tolerance (3+ seconds for complex queries)","At least 2 weeks of charging history for meaningful trend analysis"],"input_types":["Time range parameters (start_date, end_date)","Optional filters (vehicle_id, charger_type)","Aggregation granularity (daily, weekly, monthly)"],"output_types":["JSON objects with aggregated metrics: {total_cost, total_energy, avg_cost_per_kwh, avg_cost_per_mile, efficiency_trend}","Time-series arrays: [{timestamp, cost, energy, efficiency}, ...]","Summary statistics with min/max/mean values"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_2","uri":"capability://planning.reasoning.predictive.charging.recommendations.with.demand.forecasting","name":"predictive charging recommendations with demand forecasting","description":"Analyzes historical charging patterns (time of day, frequency, duration) and current battery state to generate smart charging recommendations. Uses simple heuristic-based forecasting (e.g., if user typically charges at 8 PM and it's 7:30 PM, recommend charging now) combined with optional electricity rate data to suggest optimal charging windows. Exposes recommendations as MCP tools that return actionable suggestions with reasoning, enabling agents to proactively notify users or trigger charging.","intents":["I want my AI assistant to remind me to charge before I need the car","I want to optimize charging to use cheaper off-peak electricity rates","I want to understand when I should charge based on my typical driving patterns"],"best_for":["Tesla owners with variable electricity rates (time-of-use plans)","Developers building proactive vehicle management assistants","Users seeking to minimize charging costs through behavioral optimization"],"limitations":["Forecasting is heuristic-based (pattern matching), not ML-based — accuracy depends on consistent user behavior","Requires at least 2-4 weeks of historical data to establish reliable patterns; unreliable for new users","Does not account for unexpected trips or changes in driving behavior","Electricity rate data must be manually configured or sourced externally; no automatic rate lookup","No integration with weather data or traffic predictions that might affect charging needs"],"requires":["Tessie API key with at least 2-4 weeks of charging history","Optional: electricity rate schedule (time-of-use tariff) provided by user or external service","MCP host capable of storing and updating user preferences"],"input_types":["Current battery state (percentage, estimated range)","Optional: electricity rate schedule (time-of-day rates)","Optional: user preferences (target charge level, preferred charging times)"],"output_types":["JSON recommendation object: {should_charge_now: boolean, reason: string, optimal_window: {start_time, end_time}, estimated_savings: number}","Confidence score (0-1) indicating reliability of recommendation","Explanation text suitable for user-facing messages"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_3","uri":"capability://tool.use.integration.vehicle.command.execution.with.state.validation.and.safety.checks","name":"vehicle command execution with state validation and safety checks","description":"Provides MCP tools for executing Tesla commands (lock/unlock, climate control, charging start/stop) with built-in safety validation. Before executing a command, validates preconditions (e.g., 'don't unlock if vehicle is in motion', 'don't start charging if battery is above 90%'). Translates MCP tool calls into Tessie API requests, handles authentication, and returns execution status with error details. Implements a command queue to prevent conflicting simultaneous commands.","intents":["I want my AI assistant to lock my car when I leave home","I want to remotely start charging from a conversation, but only if it's safe","I want to control climate settings via voice through an LLM agent"],"best_for":["Home automation developers integrating Tesla with voice assistants","Developers building remote vehicle management applications","Users seeking hands-free vehicle control through conversational AI"],"limitations":["Safety checks are hardcoded heuristics, not configurable — users cannot override safety rules","Command execution is asynchronous but MCP tool calls are synchronous — tool returns immediately while command executes in background, no guarantee of completion","Tessie API rate limits apply (typically 100 requests/minute) — rapid command sequences may be throttled","No rollback mechanism — if a command fails partway through (e.g., climate set to 72°F but fan speed fails), partial state changes persist","Requires Tessie account with command permissions enabled; some Tesla models or regions may not support all commands"],"requires":["Tessie API key with command execution permissions","Tesla vehicle with compatible firmware (most 2016+ models)","MCP host with ability to handle asynchronous command completion","User acknowledgment/confirmation mechanism (recommended for safety-critical commands)"],"input_types":["Command type (lock, unlock, climate_set, charge_start, charge_stop, etc.)","Command parameters (temperature for climate, charge_limit for charging)","Optional: user confirmation token for safety-critical commands"],"output_types":["JSON execution result: {success: boolean, command_id: string, status: string, error_message?: string}","Validation error with explanation if preconditions fail","Async status updates (if MCP host supports server-initiated messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_4","uri":"capability://data.processing.analysis.multi.vehicle.aggregation.and.comparison.analytics","name":"multi-vehicle aggregation and comparison analytics","description":"Aggregates data across multiple Tesla vehicles linked to a single Tessie account, enabling comparative analytics and fleet-level insights. Implements vehicle-scoped data isolation (each query specifies vehicle_id or returns data for all vehicles) and provides aggregation functions (sum, average, max, min) across vehicles. Exposes MCP tools for cross-vehicle comparisons (e.g., 'Which of my vehicles is most efficient?', 'What's the total charging cost across all cars this month?').","intents":["I own multiple Teslas and want to compare efficiency across them","I want to see total charging costs for my entire fleet","I want to identify which vehicle needs maintenance based on usage patterns"],"best_for":["Multi-vehicle Tesla owners optimizing fleet efficiency","Fleet managers tracking operational metrics across vehicles","Developers building multi-vehicle dashboards and analytics"],"limitations":["Aggregation is computed per-query, not pre-cached — complex multi-vehicle queries may take 2-5 seconds","Data consistency depends on Tessie's sync frequency with Tesla API — may be 5-15 minutes behind real-time","No built-in vehicle grouping or tagging — all vehicles treated equally, no way to segment by type or purpose","Comparison metrics are limited to available Tessie data fields; custom metrics require external computation"],"requires":["Tessie API key with multiple vehicles linked and accessible","MCP host with sufficient timeout tolerance (5+ seconds for complex multi-vehicle queries)","At least 2 vehicles for meaningful comparison"],"input_types":["Optional: vehicle_id filter (if omitted, query applies to all vehicles)","Aggregation function (sum, avg, max, min, count)","Time range for aggregation","Metric to aggregate (battery_level, charging_cost, efficiency, etc.)"],"output_types":["JSON aggregation result: {metric: string, vehicles: [{vehicle_id, value}, ...], aggregate: {sum, avg, max, min}}","Comparison matrix: [{vehicle_id, metric1, metric2, metric3}, ...]","Ranked list of vehicles by specified metric"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_5","uri":"capability://tool.use.integration.mcp.protocol.compliance.and.schema.based.tool.registration","name":"mcp protocol compliance and schema-based tool registration","description":"Implements the Model Context Protocol (MCP) specification, exposing all Tesla/Tessie capabilities as standardized MCP tools with JSON schema definitions. Each tool declares input parameters, output types, and descriptions in MCP-compliant format, enabling any MCP-compatible host (Claude Desktop, LLM frameworks) to discover and invoke tools without custom integration code. Handles MCP protocol handshake, tool listing, and result serialization according to spec.","intents":["I want to use Tessie data in Claude Desktop without writing custom code","I want to build an LLM application that can access Tesla data through standard MCP tools","I want to compose Tessie tools with other MCP servers in a multi-server setup"],"best_for":["LLM application developers using MCP-compatible hosts","Claude Desktop users wanting to add Tesla capabilities","Developers building multi-server MCP orchestrations"],"limitations":["MCP protocol overhead adds ~50-100ms per tool call compared to direct API access","Tool schemas are static at registration time — cannot dynamically add tools based on runtime conditions","MCP hosts vary in their tool-calling reliability and error handling — some may not properly handle complex nested JSON responses","No built-in MCP authentication — relies on host-level security (e.g., Claude Desktop's sandboxing)"],"requires":["MCP-compatible host (Claude Desktop 0.1.0+, or LLM framework with MCP support)","Node.js 16+ to run MCP server","Tessie API key configured in environment or config file"],"input_types":["MCP tool call requests (JSON-RPC format)","Tool parameters matching declared schema"],"output_types":["MCP tool results (JSON-RPC response format)","Tool schemas (JSON Schema format for discovery)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexcz-a11y-tessie-mcp-fixx__cap_6","uri":"capability://safety.moderation.error.handling.and.graceful.degradation.with.fallback.strategies","name":"error handling and graceful degradation with fallback strategies","description":"Implements comprehensive error handling for Tessie API failures, network timeouts, and rate limiting. When primary operations fail, applies fallback strategies: returns cached data if available, degrades to read-only mode if write operations fail, or returns partial results if some vehicles are unreachable. Provides detailed error messages to agents explaining why operations failed and suggesting remediation (e.g., 'Rate limit exceeded, try again in 30 seconds').","intents":["I want my Tesla agent to continue functioning even if Tessie API is temporarily down","I want clear error messages when operations fail so I can understand what went wrong","I want the agent to retry failed operations automatically with exponential backoff"],"best_for":["Developers building production LLM applications that require reliability","Users wanting resilient Tesla integration that handles API outages gracefully","Teams needing detailed error diagnostics for debugging integration issues"],"limitations":["Fallback to cached data may be stale (age depends on cache TTL, typically 5-15 minutes)","Automatic retries add latency (exponential backoff: 1s, 2s, 4s, 8s) — slow for time-sensitive operations","Partial results (e.g., data from 3 of 4 vehicles) may be misleading if agent doesn't understand which vehicles failed","No persistent error logging — errors are reported to agent but not stored for later analysis"],"requires":["MCP host with timeout tolerance for retry logic (up to 15+ seconds for max backoff)","Optional: external cache store (Redis, etc.) for cross-session caching; defaults to in-memory cache","Tessie API key with sufficient rate limits for retry attempts"],"input_types":["Failed operation parameters (for retry)","Optional: cache policy (use_cache: true/false, max_age_seconds)"],"output_types":["JSON error object: {error_code, message, retry_after_seconds, fallback_data?: {...}}","Partial result with error details: {success_vehicles: [...], failed_vehicles: [{id, error}], partial_data: {...}}","Retry status updates (if async retries are enabled)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Tessie API key with valid Tesla vehicle linked","MCP-compatible host (Claude Desktop, LLM frameworks supporting MCP protocol)","Network connectivity to Tessie API endpoints","Node.js 16+ (if running as standalone MCP server)","Tessie API key with historical charging data available","MCP host with sufficient timeout tolerance (3+ seconds for complex queries)","At least 2 weeks of charging history for meaningful trend analysis","Tessie API key with at least 2-4 weeks of charging history","Optional: electricity rate schedule (time-of-use tariff) provided by user or external service","MCP host capable of storing and updating user preferences"],"failure_modes":["Depends on Tessie API availability and rate limits (typically 100 requests/minute per vehicle)","No local caching — every MCP tool call hits Tessie's servers, adding 200-500ms latency","Only supports vehicles linked to Tessie account; requires prior Tesla authentication outside this MCP","Real-time updates are pull-based (agent must call tools), not push-based (no webhooks)","Requires historical charging data in Tessie; limited to data available in Tessie's database (typically 6-12 months)","Aggregation is computed on-demand per MCP call, not pre-computed — complex queries may take 1-3 seconds","No support for custom time windows or granular filtering (e.g., by charger type, location); only predefined buckets","Cost calculations depend on accurate Tessie pricing data; may not reflect actual utility rates if user has variable-rate plans","Forecasting is heuristic-based (pattern matching), not ML-based — accuracy depends on consistent user behavior","Requires at least 2-4 weeks of historical data to establish reliable patterns; unreliable for new users","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:15.092Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alexcz-a11y-tessie-mcp-fixx","compare_url":"https://unfragile.ai/compare?artifact=alexcz-a11y-tessie-mcp-fixx"}},"signature":"9jyXpdcOI2U2mHZ+HPaDtzx9vboYguvtk3HqIpyyd3fw1P+T4CUZWeEmcvP0wY3tpA9z/8trPt1F4sUn+JRMDw==","signedAt":"2026-06-21T12:41:08.730Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alexcz-a11y-tessie-mcp-fixx","artifact":"https://unfragile.ai/alexcz-a11y-tessie-mcp-fixx","verify":"https://unfragile.ai/api/v1/verify?slug=alexcz-a11y-tessie-mcp-fixx","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}