| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes 20+ discrete Docker operations (container lifecycle, image management, network/volume operations) as MCP tools with standardized request/response handling. Each tool is registered via @app.call_tool() decorator, validates inputs using Pydantic schemas from input_schemas.py, executes operations through the Docker Python SDK (v7.1.0+), and serializes responses using output_schemas.py. Supports both local Unix socket and remote SSH connections via DOCKER_HOST environment variable.
Unique: Implements MCP tool registration with Pydantic-based input validation and Docker SDK integration in a single Python package, supporting both local and remote Docker connections via environment variables. The @app.call_tool() decorator pattern with separate input_schemas.py and output_schemas.py modules provides type-safe, self-documenting tool definitions that Claude can introspect.
vs alternatives: More lightweight than Docker API wrappers like Portainer because it operates as a stateless MCP server over stdio rather than requiring a persistent web service, and more accessible than raw Docker CLI because it exposes operations as natural-language-callable tools with built-in validation.
Implements a two-phase infrastructure change pattern where the LLM first queries current Docker state using tools like list_containers(), generates a human-readable plan describing desired changes, presents the plan to the user for review, and only executes approved operations. This is registered as an MCP prompt (docker_compose) that guides the LLM through state inspection, planning, and conditional execution. The workflow prevents accidental destructive operations by requiring explicit user approval before applying changes.
Unique: Embeds a plan+apply safety pattern directly into the MCP prompt layer, allowing the LLM to inspect current state, generate plans, and wait for user approval before executing Docker operations. This is distinct from imperative Docker CLI tools because it creates a deliberate checkpoint between planning and execution, reducing risk of accidental infrastructure changes.
vs alternatives: Safer than direct Docker CLI automation because it requires explicit user approval of generated plans before execution, and more transparent than Terraform because the plan is generated in natural language and presented for human review before applying.
The server is a Python 3.12+ application that communicates with MCP clients over stdin/stdout using JSON-RPC protocol. The server runs as a long-lived process that reads MCP requests from stdin, processes them (validating inputs, executing Docker operations, serializing outputs), and writes responses to stdout. This stdio-based communication model enables the server to be launched by MCP clients (e.g., Claude Desktop) without requiring separate network infrastructure — the client spawns the server as a subprocess and pipes requests/responses through standard streams.
Unique: Uses Python 3.12+ with stdio-based JSON-RPC communication to enable subprocess-based MCP server deployment without requiring network configuration, allowing Claude Desktop and other clients to spawn the server directly
vs alternatives: Simpler to deploy than network-based servers because no port configuration is needed, and more secure than exposed network services because communication is confined to subprocess pipes
The server uses the Docker Python SDK (7.1.0+) to abstract Docker daemon API interactions. Rather than constructing raw HTTP requests to the Docker daemon, the server calls SDK methods like docker.containers.run(), docker.images.pull(), docker.networks.create(), etc. The SDK handles connection pooling, request serialization, response parsing, and error handling. This abstraction layer insulates the MCP server from Docker API versioning and protocol details, allowing it to work with different Docker daemon versions without code changes.
Unique: Uses Docker Python SDK (7.1.0+) to abstract daemon API interactions, providing connection pooling and error handling without requiring raw HTTP request construction, enabling compatibility with multiple Docker daemon versions
vs alternatives: More maintainable than raw Docker API calls because the SDK handles versioning and protocol details, and more reliable than subprocess-based docker CLI calls because the SDK uses persistent connections
Exposes container logs and performance metrics (CPU, memory, network I/O) as MCP resources that stream data in real-time. Implemented via @app.read_resource() handlers that connect to the Docker daemon's log and stats APIs, format output as text or structured data, and push updates to the MCP client. Resources are identified by container ID and can be subscribed to for continuous monitoring without polling.
Unique: Leverages MCP's resource streaming capability to expose Docker logs and stats as first-class resources that can be subscribed to, rather than polling-based tool calls. This allows the LLM client to receive continuous updates without repeated tool invocations, reducing latency and server load.
vs alternatives: More efficient than repeated tool calls to fetch logs because it uses MCP resource subscriptions for streaming, and more integrated than external monitoring tools (Prometheus, ELK) because logs and stats are available directly within the LLM context without additional infrastructure.
Provides granular control over container lifecycle through discrete MCP tools (run_container, start_container, stop_container, restart_container, remove_container). Each operation accepts configuration parameters (image, ports, environment variables, volumes, resource limits) as Pydantic-validated inputs, executes through the Docker Python SDK, and returns container ID or status. Supports both simple operations (stop a running container) and complex configurations (run with custom networks, mounts, and resource constraints).
Unique: Decomposes container lifecycle into discrete, independently-callable MCP tools rather than a monolithic 'manage container' function. Each tool (run, start, stop, restart, remove) is individually registered with its own Pydantic schema, allowing the LLM to compose complex workflows by chaining tool calls and inspecting intermediate results.
vs alternatives: More granular than Docker Compose because each operation is a separate tool call with explicit parameters, and more accessible than Docker CLI because configuration is validated and documented through Pydantic schemas that Claude can introspect.
Exposes Docker image operations as MCP tools: pull_image (fetch from registry), build_image (build from Dockerfile), list_images (enumerate local images), inspect_image (get metadata), remove_image (delete). Each tool validates inputs via Pydantic, executes through Docker SDK, and returns structured metadata (image ID, tags, size, creation date). Build operations accept Dockerfile content or path and build context; pull operations support authentication via registry credentials.
Unique: Separates image operations into distinct tools (pull, build, list, inspect, remove) rather than a monolithic image manager, allowing the LLM to compose workflows like 'build image → tag it → run container from it' by chaining tool calls. Build operations accept Dockerfile content directly, enabling dynamic image generation without filesystem access.
vs alternatives: More flexible than Docker Compose for image management because individual tools can be called independently, and more accessible than Docker CLI because Pydantic schemas document all parameters and validation rules that Claude can introspect.
Provides MCP tools for Docker network and volume operations: create_network (define custom networks), list_networks/list_volumes (enumerate infrastructure), inspect_network/inspect_volume (get metadata), remove_network/remove_volume (delete), connect_container_to_network (attach running containers). Each operation validates inputs via Pydantic, executes through Docker SDK, and returns structured metadata. Supports network drivers (bridge, overlay, host) and volume drivers (local, named).
Unique: Exposes Docker's network and volume abstractions as discrete MCP tools that can be composed to build infrastructure. The connect_container_to_network tool allows dynamic network attachment without container restart, enabling runtime topology changes that would require orchestration in other systems.
vs alternatives: More granular than Docker Compose for infrastructure management because networks and volumes can be created and modified independently of containers, and more accessible than raw Docker API because Pydantic schemas document all options and validation rules.
+4 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.
Docker MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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