| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Google Cloud Run deployment operations as callable MCP tools through a centralized tool registry (tools.js) that coordinates with specialized deployment modules. The system implements the Model Context Protocol specification to enable AI agents and assistants to invoke deployment operations via a standardized interface, with tools dynamically registered based on operational mode (local vs. remote) determined by gcp-metadata.js environment detection.
Unique: Implements dual-mode tool registration (local vs. remote) with environment-aware security boundaries through gcp-metadata.js detection, rather than static tool sets. Tools are registered conditionally based on deployment context, enabling the same MCP server to operate securely in both local development and Cloud Run production environments.
vs alternatives: Provides MCP-native tool exposure for Cloud Run operations, enabling direct integration with MCP clients like Claude Desktop and Cline, whereas direct GCP SDK usage requires custom wrapper code in each client application.
Deploys containerized applications to Cloud Run by accepting application source code as file contents (not file paths), orchestrating a multi-step pipeline through cloud-run-deploy.js that coordinates Cloud Build for container compilation, Artifact Registry for image storage, and Cloud Run service creation. This approach enables deployment without requiring local filesystem access, supporting both inline code submission and remote deployment scenarios.
Unique: Accepts application code as inline file contents rather than filesystem paths, enabling deployment from generated or in-memory code without requiring local file I/O. The deployment pipeline (cloud-run-deploy.js) orchestrates Cloud Build, Artifact Registry, and Cloud Run APIs in sequence, abstracting the multi-service coordination required for containerized deployment.
vs alternatives: Enables code-to-deployment in a single MCP tool call without intermediate file writes, whereas gcloud CLI requires local Dockerfile and manual build/push/deploy steps. Faster for AI-driven workflows that generate code in-memory.
Deploys applications from local filesystem sources through two specialized tools (deploy-local-files and deploy-local-folder) that read source code directly from disk and submit to the Cloud Run deployment pipeline. These tools are local-mode-only and integrate with cloud-run-deploy.js to handle file discovery, validation, and submission to Cloud Build, enabling developers to deploy existing local projects without manual file content extraction.
Unique: Provides two separate tools for file-level (deploy-local-files) and directory-level (deploy-local-folder) deployment, with automatic file discovery and aggregation before submission to the deployment pipeline. This dual-tool approach allows granular control over what gets deployed while maintaining simplicity for directory-based workflows.
vs alternatives: Eliminates manual file content extraction required by deploy-file-contents tool, enabling one-command deployment of existing projects. More convenient than gcloud CLI for AI agent workflows that need to deploy local projects discovered at runtime.
Lists and retrieves detailed metadata about deployed Cloud Run services through cloud-run-services.js, providing two complementary tools: list-services returns all services in a project with summary information, while get-service retrieves detailed configuration for a specific service including environment variables, resource allocation, and traffic routing. Both tools query the @google-cloud/run SDK and are available in both local and remote modes.
Unique: Provides both list and detail operations through separate tools, allowing AI agents to first discover services (list-services) and then retrieve detailed configuration for specific services (get-service) without requiring multiple API calls or filtering logic. Integrates directly with @google-cloud/run SDK for authoritative service state.
vs alternatives: Exposes service metadata as callable MCP tools, enabling AI agents to inspect deployments without learning gcloud CLI syntax. More efficient than shell command execution for programmatic service discovery and configuration inspection.
Manages GCP project lifecycle through two local-mode-only tools (list-projects and create-project) that integrate with gcp-projects.js and the @google-cloud/resource-manager SDK. list-projects enumerates all projects accessible to the authenticated user, while create-project provisions new GCP projects with automatic billing account association and API enablement through @google-cloud/service-usage. These tools enable AI agents to discover or provision projects without manual GCP console interaction.
Unique: Implements local-mode-only restriction for project creation via gcp-metadata.js environment detection, preventing remote Cloud Run instances from provisioning new projects (security boundary). Automatically associates billing accounts and enables required APIs during project creation, abstracting multi-step GCP setup into a single tool call.
vs alternatives: Enables programmatic project provisioning without gcloud CLI or GCP console access, allowing AI agents to create isolated environments for deployments. Automatic API enablement reduces setup friction compared to manual gcloud commands.
Automatically detects deployment context (local development vs. Cloud Run production) through gcp-metadata.js by querying GCP metadata service, and conditionally registers tools based on detected mode. Local mode enables all tools including project management and filesystem access; remote mode (when running on Cloud Run) restricts to deployment and service query tools only. This pattern implements security boundaries without requiring manual configuration, enabling the same MCP server binary to operate safely in both contexts.
Unique: Implements automatic context detection via GCP metadata service queries rather than explicit configuration, enabling zero-config deployment of the same MCP server to both local and Cloud Run environments with appropriate security boundaries. The gcp-metadata.js module encapsulates detection logic, allowing tools.js to conditionally register capabilities without hardcoded environment checks.
vs alternatives: Eliminates need for separate local and remote server builds or configuration files. Provides automatic security enforcement (project tools disabled in remote mode) without requiring manual policy configuration, reducing misconfiguration risk compared to environment variable-based mode selection.
Coordinates multiple Google Cloud service SDKs (@google-cloud/cloudbuild, @google-cloud/storage, @google-cloud/artifact-registry, @google-cloud/run) through cloud-run-deploy.js to implement a complete deployment pipeline: submits source code to Cloud Build for container compilation, stores build artifacts in Artifact Registry, and creates/updates Cloud Run services. The orchestration handles sequencing, error propagation, and result aggregation across services, abstracting the complexity of multi-service coordination from MCP tool callers.
Unique: Encapsulates multi-service orchestration logic in cloud-run-deploy.js, allowing MCP tools to invoke deployment as a single operation without exposing Cloud Build, Artifact Registry, or Cloud Run APIs separately. The module handles service sequencing, credential passing, and result aggregation, reducing complexity for MCP tool implementations.
vs alternatives: Provides unified deployment pipeline through single MCP tool call, whereas manual gcloud commands require separate build, push, and deploy steps. Abstracts service coordination details, making deployment accessible to AI agents without GCP service knowledge.
Implements HTTP server transport for MCP protocol using Express.js, enabling the MCP server to run on Cloud Run and accept remote MCP client connections over HTTP. The mcp-server.js entry point conditionally initializes Express server when running in remote mode (detected via gcp-metadata.js), exposing MCP protocol endpoints for tool invocation and resource access. This transport mechanism enables multi-user access to a single MCP server instance running on Cloud Run.
Unique: Conditionally initializes Express HTTP server only in remote mode (Cloud Run environment), determined by gcp-metadata.js detection. This dual-transport approach (stdio for local, HTTP for remote) enables the same mcp-server.js entry point to serve both local development and remote production scenarios without code branching.
vs alternatives: Enables remote MCP server deployment without separate HTTP wrapper code. Provides HTTP transport natively through Express, whereas alternative approaches might require additional reverse proxy or API gateway configuration.
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 Google Cloud Run 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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