Supabase ranks higher at 42/100 vs Tessie Insights （fork from keithah/tessie-mcp） at 30/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Tessie Insights （fork from keithah/tessie-mcp） | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 30/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Implements MCP as a thin adapter layer over Tessie's REST API, allowing LLM agents to treat Tesla vehicle state as native tools rather than requiring custom API integration code. Uses MCP's standardized tool schema to expose vehicle commands and queries, enabling any MCP-compatible host to access Tesla data without SDK changes.
vs alternatives: Simpler than building direct Tesla API integrations because Tessie handles authentication and unofficial endpoint management; more flexible than Tesla's official mobile app because it exposes data as composable MCP tools for agents rather than fixed UI flows.
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?'.
Unique: Implements client-side time-series bucketing and aggregation logic rather than relying on Tessie's backend to provide pre-aggregated data. Allows LLM agents to ask natural-language questions about charging costs ('Was I more efficient last month?') and translates them into aggregation queries, enabling conversational analytics without requiring agents to understand data structures.
vs alternatives: More accessible than writing custom SQL queries against raw charging logs; provides Tesla-specific cost metrics (cost-per-mile) that generic analytics tools don't understand natively.
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.
Unique: Implements pattern-based forecasting directly in the MCP tool layer, allowing agents to reason about charging decisions without external ML infrastructure. Combines historical behavior analysis with optional rate data to generate context-aware recommendations that adapt to user preferences and electricity pricing.
vs alternatives: Simpler than deploying ML models for demand forecasting; more intelligent than static charging schedules because it adapts to individual user patterns and electricity rates.
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.
Unique: Implements safety validation as a middleware layer between MCP tool calls and Tessie API execution, preventing dangerous commands before they reach the vehicle. Uses a command queue to serialize execution and prevent race conditions, ensuring state consistency even when agents issue rapid-fire commands.
vs alternatives: Safer than direct Tessie API access because it enforces precondition checks; more flexible than Tesla's official app because it allows programmatic control through natural language via LLM agents.
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?').
Unique: Implements vehicle-scoped data isolation and cross-vehicle aggregation at the MCP tool layer, allowing agents to seamlessly query single or multiple vehicles without changing tool signatures. Provides aggregation functions that work across vehicle boundaries, enabling fleet-level insights without requiring agents to manually loop through vehicles.
vs alternatives: More convenient than querying Tessie API separately for each vehicle; enables natural-language fleet analytics ('Which car is most efficient?') that generic analytics tools don't understand without custom configuration.
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.
Unique: Implements MCP as a first-class integration pattern rather than a wrapper around existing APIs. Defines tool schemas that accurately represent Tessie's capabilities and constraints, enabling MCP hosts to provide intelligent tool suggestions and error handling based on schema validation.
vs alternatives: More standardized than custom API integrations because it uses MCP's schema-based discovery; more interoperable than direct Tessie API access because any MCP-compatible host can use it without SDK changes.
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').
Unique: Implements error handling as a first-class concern with explicit fallback strategies rather than failing fast. Provides agents with enough context to make intelligent decisions about retries and degraded operation, enabling conversational error recovery ('The API is rate-limited; should I try again in 30 seconds?').
vs alternatives: More resilient than naive API wrappers that fail on first error; more informative than generic HTTP error codes because it provides Tesla/Tessie-specific context and remediation suggestions.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Tessie Insights （fork from keithah/tessie-mcp） at 30/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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