| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a FastMCP server that registers ROS operations (topics, services, parameters) as MCP tools, enabling LLMs to invoke robot commands through standardized tool-calling semantics. The server.py module acts as a central coordinator that dynamically discovers ROS system state and exposes it as callable MCP tools, translating natural language requests into ROS API calls via the rosbridge WebSocket interface without modifying existing robot code.
Unique: Uses FastMCP's tool registration pattern combined with dynamic ROS system introspection to expose the entire ROS ecosystem as callable tools without code generation — the server discovers topics/services at runtime and registers them as MCP tools on-demand, enabling zero-configuration integration with any ROS system.
vs alternatives: Differs from REST API wrappers by using MCP's native tool-calling semantics, enabling LLMs to discover and invoke ROS operations directly without custom prompt engineering or API documentation parsing.
Implements subscribe_to_topic() tool that establishes persistent WebSocket subscriptions to ROS topics via rosbridge, streaming sensor data and state updates into the LLM's context window. The WebSocket manager maintains active subscriptions and buffers incoming messages, allowing the LLM to observe robot state changes in real-time and make decisions based on current sensor readings without polling.
Unique: Combines WebSocket subscription management with LLM context injection, allowing the LLM to maintain awareness of robot state without explicit polling — subscriptions are managed by the server and new messages are automatically surfaced to the LLM as tool outputs.
vs alternatives: Enables continuous observation without requiring the LLM to repeatedly call a 'get latest sensor data' tool, reducing latency and context overhead compared to polling-based approaches.
Implements full MCP protocol compliance enabling the server to integrate with MCP-compatible LLM clients including Claude Desktop and Gemini-CLI. The server exposes tools, resources, and prompts through the MCP protocol, allowing these clients to discover and invoke ROS operations through their native tool-calling interfaces.
Unique: Implements full MCP protocol compliance with specific integrations for Claude Desktop and Gemini-CLI, enabling these clients to discover and invoke ROS operations through their native MCP tool-calling interfaces.
vs alternatives: Provides seamless integration with popular LLM clients through standard MCP protocol, avoiding custom API wrappers or client-specific implementations.
Provides Docker configurations and example scripts for running the ROS-MCP-Server with Turtlesim (simple 2D turtle simulator) and LIMO mobile robot simulator, enabling developers to test and prototype robot control without physical hardware. The examples include pre-configured ROS environments, rosbridge setup, and sample LLM prompts for controlling simulated robots.
Unique: Provides complete Docker-based simulation environments with pre-configured ROS, rosbridge, and example robots (Turtlesim, LIMO), enabling zero-setup prototyping and testing of robot control without physical hardware.
vs alternatives: Reduces setup friction compared to manual ROS installation and configuration, enabling developers to start testing immediately.
Provides integration examples and documentation for controlling the Unitree GO2 quadruped robot through ROS-MCP-Server, including hardware-specific configuration, motion primitives (walk, trot, jump), and sensor access (IMU, cameras, lidar). The integration demonstrates how to adapt the server for real robot hardware with specific API requirements and safety constraints.
Unique: Provides concrete integration examples for a real quadruped robot (Unitree GO2), demonstrating how to adapt ROS-MCP-Server for hardware-specific APIs, motion primitives, and safety constraints.
vs alternatives: Enables real-world robot deployment with LLM control, unlike simulation-only examples that don't address hardware-specific challenges.
Implements call_service() tool that dynamically generates MCP tool schemas for ROS services by introspecting their request/response message types, then marshals LLM-provided parameters into ROS service calls via rosbridge. The server discovers service signatures at runtime and binds them to MCP tool definitions, enabling the LLM to invoke services with type-safe parameter passing without manual schema definition.
Unique: Uses dynamic message introspection to generate MCP tool schemas for ROS services without pre-defined specifications — the server queries ROS service types at runtime and automatically creates type-safe tool definitions, enabling the LLM to invoke services with correct parameter binding.
vs alternatives: Avoids manual service schema definition by leveraging ROS's built-in message introspection, making the system adaptable to new services without code changes.
Implements get_param() and set_param() tools that interact with the ROS parameter server via rosbridge, automatically inferring parameter types (int, float, string, bool, list) from values. The server provides a unified interface for reading and modifying ROS parameters without requiring the LLM to specify types explicitly, enabling configuration changes and state inspection through natural language.
Unique: Implements automatic type inference for parameter values, allowing the LLM to set parameters without explicit type specification — the server infers whether a value should be int, float, string, bool, or list based on the provided value and ROS parameter server semantics.
vs alternatives: Reduces friction compared to REST APIs that require explicit type specification, making parameter manipulation more natural for LLMs.
Implements list_topics(), list_services(), list_params(), and get_topic_type() tools that query the ROS master/parameter server to enumerate available topics, services, and parameters with their types and message structures. The server performs ROS system introspection at runtime, building a dynamic map of the ROS ecosystem that the LLM can query to understand available operations before invoking them.
Unique: Provides comprehensive ROS system introspection through MCP tools, allowing the LLM to query the ROS topology dynamically without requiring pre-configured knowledge of available operations — the server acts as a bridge to ROS's native introspection APIs.
vs alternatives: Enables zero-configuration integration by allowing the LLM to discover the ROS system at runtime, unlike static API documentation or hardcoded tool lists.
+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 ros-mcp-server at 33/100.
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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