| Ecosystem |
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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates conversational AI commands into structured ThingsBoard REST API operations through a Spring Boot MCP server that parses natural language intent, maps it to tool schemas, and executes authenticated API calls. The server acts as a semantic bridge between LLM outputs and IoT platform operations, handling JWT authentication, request serialization, and response transformation without requiring users to write API code directly.
Unique: Implements MCP protocol as a Spring Boot application with edition-aware tool providers that dynamically expose different tool sets for Community Edition vs Professional Edition ThingsBoard instances, enabling single deployment to serve heterogeneous ThingsBoard deployments with appropriate capability filtering
vs alternatives: Provides standardized MCP protocol integration (vs proprietary API wrappers) with native support for multiple ThingsBoard editions and deployment modes (STDIO, HTTP/SSE) in a single open-source package
Exposes device CRUD operations (create, read, update, delete) and state management via MCP tools that accept natural language parameters and translate them to ThingsBoard Device API calls. Handles device provisioning, attribute assignment, and credential management through a tool callback provider that validates inputs and manages JWT-authenticated API requests to the ThingsBoard REST endpoint.
Unique: Implements edition-aware device tools that expose different capabilities for CE vs PE (e.g., entity groups only in PE), with a Tool Callback Provider pattern that validates natural language parameters against ThingsBoard schema before API execution, preventing invalid requests from reaching the backend
vs alternatives: Provides conversational device management (vs manual REST calls or CLI scripts) with built-in schema awareness and permission validation, reducing provisioning errors and enabling non-technical operators to manage devices
Generates MCP-compliant tool schemas that describe available tools, their parameters, and expected outputs, enabling LLM clients to discover and understand tool capabilities through the MCP discovery protocol. The implementation uses a Tool Callback Provider pattern that introspects tool implementations and generates JSON schemas that conform to MCP specifications, allowing LLMs to invoke tools with proper parameter validation.
Unique: Implements MCP tool discovery through a Tool Callback Provider pattern that generates JSON schemas from tool implementations, enabling LLM clients to understand tool capabilities and parameters without manual schema definition
vs alternatives: Provides automatic tool schema generation (vs manual schema definition) with MCP protocol compliance, reducing schema maintenance burden and enabling dynamic tool discovery
Packages ThingsBoard MCP as a Spring Boot application deployable via Docker containers or standalone JAR files with configurable application properties. The implementation uses Spring Boot's auto-configuration and property binding to enable deployment flexibility, supporting both containerized cloud deployments and traditional JAR-based installations with environment-based configuration.
Unique: Implements Spring Boot application with dual deployment modes (Docker and JAR) using property-based configuration that enables environment-specific deployments without code changes, supporting both containerized cloud environments and traditional server deployments
vs alternatives: Provides flexible deployment options (Docker and JAR) with Spring Boot configuration management, enabling deployment to diverse environments (cloud, on-premise, edge) without code modification
Provides configurable logging at multiple levels (DEBUG, INFO, WARN, ERROR) with diagnostic output for troubleshooting MCP server issues, API communication, and authentication problems. The implementation uses Spring Boot's logging framework with configuration options for log levels, output formats, and diagnostic logging that helps developers understand request/response flows and identify integration issues.
Unique: Implements Spring Boot logging with configurable diagnostic output for MCP protocol messages and ThingsBoard API communication, enabling developers to trace request flows and identify integration issues without code instrumentation
vs alternatives: Provides comprehensive logging and diagnostics (vs silent failures or minimal error messages) with configurable verbosity, enabling faster troubleshooting and reducing mean-time-to-resolution for integration issues
Enables querying of ThingsBoard assets and entity relationships through a sophisticated Entity Data Query (EDQ) system that translates natural language filter expressions into structured query objects. The system supports complex filtering (equality, range, text search, regex), sorting, pagination, and relationship traversal through a query builder that constructs REST API payloads without exposing SQL or API syntax to users.
Unique: Implements a dedicated Entity Data Query (EDQ) and Entity Count Query (ECQ) system with support for multiple filter types (equality, range, text search, regex) and a query builder pattern that constructs REST API payloads dynamically based on natural language intent, with built-in pagination and sorting support
vs alternatives: Provides natural language entity querying (vs SQL or REST API syntax) with sophisticated filtering capabilities and relationship traversal, enabling non-technical users to perform complex data analysis without database knowledge
Exposes ThingsBoard telemetry APIs through MCP tools that retrieve time-series data for devices and assets with natural language time range specifications and aggregation options. The implementation handles timestamp parsing, data point filtering, and metric aggregation (min, max, avg, sum) through a Telemetry Tool that translates conversational requests into ThingsBoard REST API calls with proper JWT authentication and response formatting.
Unique: Implements natural language time-range parsing (e.g., 'last 24 hours', 'between Jan 1 and Jan 31') with automatic timestamp conversion and support for ThingsBoard's built-in aggregation functions, enabling non-technical users to perform time-series analysis without timestamp manipulation
vs alternatives: Provides conversational telemetry access (vs direct REST API or database queries) with natural language time specifications and automatic aggregation, reducing data analysis friction for non-technical operators
Exposes ThingsBoard alarm lifecycle operations (create, acknowledge, clear, delete) and querying through MCP Alarm Tools that translate natural language commands into REST API calls. The implementation handles alarm state transitions, severity filtering, and temporal queries through a tool callback provider that validates alarm parameters and manages JWT-authenticated requests to ThingsBoard's Alarm API endpoint.
Unique: Implements Alarm Tools with natural language state transition support (acknowledge, clear, delete) and temporal filtering, allowing conversational alarm management without requiring knowledge of ThingsBoard alarm API semantics or state machine details
vs alternatives: Provides conversational alarm management (vs manual dashboard interaction or API calls) with natural language severity and status filtering, enabling faster incident response through AI-assisted operations
+5 more capabilities
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 ThingsBoard at 27/100.
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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
+1 more capabilities