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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Manages Docker container lifecycle (create, start, stop, restart, remove) through MCP protocol bindings that translate high-level container operations into Docker daemon API calls. Implements stateless request-response patterns where each MCP message maps to a specific Docker API endpoint, enabling remote container orchestration without maintaining persistent connections or container state in the MCP server itself.
Unique: Exposes Docker container lifecycle operations through MCP protocol, allowing LLM agents to manage containers as first-class tools rather than shell commands, with structured request/response semantics that preserve Docker API semantics while adapting to MCP's message-based architecture.
vs alternatives: Enables LLM agents to manage containers with semantic understanding (vs. shell command execution), while remaining protocol-agnostic through MCP abstraction (vs. Docker SDK bindings locked to specific languages).
Orchestrates multi-container applications defined in docker-compose.yml files through MCP endpoints that parse compose manifests and translate high-level compose operations (up, down, restart services) into coordinated Docker API calls. Maintains awareness of service dependencies and health states as defined in the compose file, enabling intelligent orchestration of interconnected services.
Unique: Parses docker-compose.yml manifests to understand service topology and dependencies, then exposes compose operations through MCP as structured tools rather than shell commands, enabling LLM agents to reason about multi-container deployments as semantic units.
vs alternatives: Provides compose-aware orchestration (vs. generic container management), allowing agents to understand service relationships and health states, while remaining language-agnostic through MCP (vs. Docker SDK bindings).
Streams and retrieves container logs through MCP endpoints that attach to running containers' stdout/stderr streams or fetch historical logs from the Docker daemon's log driver. Implements both real-time streaming (via MCP message streaming or polling) and historical retrieval with filtering by timestamp, log level, or search patterns, without requiring direct container shell access.
Unique: Exposes Docker log streams through MCP protocol with support for both real-time streaming and historical retrieval, allowing LLM agents to access container diagnostics without shell access or log aggregation infrastructure, while respecting Docker's native log driver architecture.
vs alternatives: Provides direct access to Docker's native logs (vs. requiring external log aggregation like ELK), while enabling LLM agents to reason about logs as structured data (vs. raw shell output).
Inspects Docker images and retrieves detailed metadata (layers, environment variables, exposed ports, entry points, build history) through MCP endpoints that query the Docker daemon's image inspection API. Enables agents to understand image composition and configuration without pulling or running images, supporting image discovery and validation workflows.
Unique: Provides structured image metadata inspection through MCP, allowing LLM agents to reason about image composition and configuration as semantic data rather than raw Docker CLI output, with support for layer-level analysis.
vs alternatives: Enables agents to validate images before deployment (vs. discovering issues at runtime), while remaining protocol-agnostic through MCP (vs. Docker SDK bindings).
Monitors container resource usage (CPU, memory, network I/O, disk I/O) through MCP endpoints that poll the Docker daemon's stats API and expose real-time or historical metrics. Implements periodic sampling of container stats without requiring persistent monitoring agents, enabling LLM agents to assess container health and performance characteristics.
Unique: Exposes Docker container stats through MCP with support for both real-time polling and historical sampling, enabling LLM agents to assess container health and performance without external monitoring infrastructure, while maintaining stateless request-response semantics.
vs alternatives: Provides direct access to Docker's native metrics (vs. requiring Prometheus or other monitoring stacks), while enabling agents to reason about performance as structured data (vs. raw CLI output).
Inspects and configures Docker networks through MCP endpoints that query and modify network topology, including listing networks, inspecting network details (connected containers, IP ranges, driver), and connecting/disconnecting containers from networks. Enables agents to understand and modify container networking without direct network configuration commands.
Unique: Exposes Docker network inspection and container attachment through MCP, allowing LLM agents to reason about and modify container networking topology as semantic operations rather than low-level network commands.
vs alternatives: Enables agents to manage container networking without shell access or network configuration expertise (vs. direct network commands), while remaining protocol-agnostic through MCP.
Manages and inspects Docker volumes through MCP endpoints that list volumes, inspect volume metadata (mount point, driver, labels), and attach/detach volumes from containers. Provides visibility into persistent storage configuration without requiring filesystem access, enabling agents to understand and manage data persistence for containerized applications.
Unique: Exposes Docker volume inspection and container attachment through MCP, allowing LLM agents to reason about persistent storage configuration and manage volume lifecycle as semantic operations.
vs alternatives: Provides structured volume metadata access (vs. raw filesystem inspection), enabling agents to understand data persistence without direct filesystem access.
Implements MCP protocol bindings that register Docker operations as callable tools with structured schemas, enabling MCP-compatible clients (Claude, custom hosts) to discover and invoke Docker capabilities through standardized tool-calling interfaces. Uses JSON Schema to define input/output contracts for each Docker operation, allowing clients to validate requests and responses.
Unique: Implements MCP protocol bindings that expose Docker operations as first-class tools with JSON Schema contracts, enabling LLM agents to discover and invoke Docker capabilities through standardized tool-calling interfaces rather than shell commands or SDK bindings.
vs alternatives: Enables semantic tool calling for Docker operations (vs. shell command execution), while remaining client-agnostic through MCP protocol (vs. language-specific SDK bindings).
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 Docker at 21/100.
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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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