| 0 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Automates Supabase PostgreSQL schema migrations by introspecting the current database state, comparing against desired schema definitions, and generating idempotent SQL migrations. Uses MCP tool protocol to expose migration operations that detect structural changes (tables, columns, constraints, indexes) and generate safe ALTER statements with rollback capability. Bidirectional sync ensures schema definitions stay synchronized with runtime database state without manual intervention.
Unique: Exposes schema migration as MCP tools rather than CLI commands, enabling AI agents and LLMs to autonomously detect schema drift and generate migrations within agentic workflows without subprocess calls or external orchestration
vs alternatives: Unlike Prisma Migrate or Liquibase which require explicit migration files, Supabase Admin infers migrations from schema state comparison, reducing boilerplate while maintaining safety through MCP's structured tool protocol
Introspects Supabase PostgreSQL schema and generates strongly-typed TypeScript interfaces, enums, and utility types that match table structures, relationships, and constraints. Simultaneously scaffolds CRUD operation functions (insert, select, update, delete) with type-safe parameters and return types, leveraging Supabase's PostgREST API. Generated code includes proper null handling, optional fields based on constraints, and relationship types for foreign keys.
Unique: Generates types and CRUD functions as MCP tool outputs that can be invoked by AI agents, enabling autonomous code generation workflows where LLMs can inspect schema and produce application code without manual schema documentation
vs alternatives: More integrated than Supabase's CLI type generation because it operates within MCP protocol, allowing real-time schema inspection and code generation as part of agentic workflows rather than as separate CLI invocations
Aggregates Supabase audit logs (Auth events, API calls, database changes) through MCP tools that provide queryable access to compliance-relevant events. Implements data lineage tracking to understand which users modified which records and when, enabling forensic analysis. Generates compliance reports (SOC 2, HIPAA, GDPR) with configurable retention and export formats for regulatory submissions.
Unique: Exposes audit logging and compliance reporting as MCP tools that enable AI agents to autonomously generate compliance reports and investigate security incidents, with data lineage tracking for forensic analysis
vs alternatives: More integrated than external audit logging services because MCP tools have native access to Supabase's internal audit logs and can track data lineage through RLS policies, while still allowing export to external compliance systems
Manages Supabase PostgreSQL roles, permissions, and RLS policies through MCP tools that abstract the complexity of SQL GRANT statements and policy creation. Enables defining role hierarchies, assigning permissions to tables/functions, and creating RLS policies that enforce row-level data access based on user context (auth.uid(), auth.jwt()). Validates policy logic before deployment and provides policy testing capabilities to verify access control behavior.
Unique: Exposes RLS policy creation and testing as MCP tools that can be invoked by AI agents to autonomously design and validate access control policies based on application requirements, rather than requiring manual SQL policy writing
vs alternatives: More accessible than raw SQL policy management because MCP tools abstract GRANT/REVOKE syntax and provide policy validation, while still maintaining full PostgreSQL RLS expressiveness unlike simplified permission systems
Manages Supabase Auth configuration (providers, settings, JWT secrets) and application secrets through MCP tools that enforce secure storage and rotation patterns. Integrates with Supabase's secret management to store API keys, database credentials, and third-party service tokens without exposing them in code or logs. Supports automated secret rotation schedules and provides audit trails of all secret access and modifications.
Unique: Integrates secret management with MCP protocol, allowing AI agents to autonomously configure Auth providers and rotate secrets based on policies without exposing credentials in agent logs or context windows
vs alternatives: More integrated than managing secrets via Supabase dashboard because MCP tools enable programmatic secret rotation and audit trail queries, while maintaining Supabase's native secret encryption at rest
Exposes Supabase project metrics (database connections, query performance, storage usage, API request rates) through MCP tools that aggregate data from Supabase's monitoring APIs and PostgreSQL system tables. Implements automated health checks that monitor database availability, replication lag, and resource utilization, with configurable alerting thresholds. Provides historical metrics data for trend analysis and capacity planning.
Unique: Aggregates Supabase monitoring APIs and PostgreSQL system tables into MCP tools that can be invoked by AI agents to autonomously detect performance issues and trigger remediation workflows without manual dashboard checks
vs alternatives: More integrated than external monitoring tools (Datadog, New Relic) because it operates within MCP protocol and has native access to Supabase's internal metrics, while still allowing integration with external alerting systems
Manages Supabase Storage buckets through MCP tools that handle bucket creation, configuration, and file lifecycle policies (expiration, archival). Implements access control by creating signed URLs with configurable expiration times and permissions, and manages bucket-level policies for public/private access. Supports automated cleanup of expired files and provides storage usage analytics per bucket.
Unique: Exposes storage bucket management and signed URL generation as MCP tools, enabling AI agents to autonomously manage file access policies and lifecycle without requiring separate S3 SDK calls or dashboard interactions
vs alternatives: More integrated than raw S3 SDK because MCP tools abstract Supabase Storage's specific API and provide lifecycle automation, while maintaining compatibility with S3-compatible storage patterns
Configures Supabase Realtime subscriptions through MCP tools that manage PostgreSQL LISTEN/NOTIFY channels and broadcast subscriptions. Enables defining which tables/events trigger real-time updates, configuring message filtering and transformation, and managing subscription lifecycle. Provides tools to test real-time event delivery and monitor active subscriptions for debugging connection issues.
Unique: Exposes Realtime subscription configuration as MCP tools that can be invoked by AI agents to autonomously set up event streaming based on application requirements, with built-in testing and debugging capabilities
vs alternatives: More accessible than raw PostgreSQL LISTEN/NOTIFY because MCP tools abstract Supabase Realtime's API and provide subscription testing, while maintaining full event filtering and transformation capabilities
+3 more capabilities
Scrapes a single URL and converts HTML content to clean markdown using Firecrawl's content extraction pipeline. The firecrawl_scrape tool accepts a URL and optional parameters (formats, headers, wait time, screenshot capability) and returns structured markdown output with automatic cleanup of boilerplate, navigation, and ads. Implements MCP tool handler pattern that marshals arguments through the @mendable/firecrawl-js client library to Firecrawl's backend processing engine.
Unique: Integrates Firecrawl's proprietary content extraction engine (which uses ML-based boilerplate removal and semantic content identification) through MCP protocol, enabling AI agents to access production-grade web scraping without managing browser automation or parsing logic themselves. The markdown conversion is handled server-side rather than client-side, reducing latency and ensuring consistent output formatting.
vs alternatives: Cleaner markdown output than regex-based scrapers like Cheerio or Puppeteer-only solutions because Firecrawl uses ML models to identify main content; simpler than self-hosted solutions because it's fully managed and requires only an API key.
Scrapes multiple URLs in a single operation using Firecrawl's batch processing pipeline. The firecrawl_batch_scrape tool accepts an array of URLs and shared options, submitting them to Firecrawl's backend which processes them in parallel and returns an array of markdown-converted content objects. Implements batching through the @mendable/firecrawl-js client's batch method, which handles request queuing, parallel execution, and result aggregation without requiring client-side coordination.
Unique: Implements server-side parallel batch processing through Firecrawl's backend rather than client-side loop iteration, reducing network round-trips and enabling true concurrent scraping. The batch operation is atomic from the MCP client perspective — a single tool call returns all results, simplifying agent orchestration logic.
More efficient than sequential scraping loops because Firecrawl handles parallelization server-side; simpler than managing Promise.all() with individual scrape calls because batching is a first-class operation with built-in error handling.
Firecrawl MCP Server scores higher at 62/100 vs Supabase Admin at 32/100. Supabase Admin leads on ecosystem, while Firecrawl MCP Server is stronger on adoption and quality.
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Packages the Firecrawl MCP server as a Docker container with environment-based configuration, enabling deployment to containerized infrastructure (Kubernetes, Docker Compose, cloud platforms). The Dockerfile builds a Node.js runtime with the server code and exposes configuration through environment variables, allowing operators to deploy without modifying code. Supports both cloud and self-hosted Firecrawl instances through configuration.
Unique: Provides production-ready Docker packaging with environment-based configuration, enabling zero-code deployment to containerized infrastructure. The Dockerfile handles Node.js runtime setup and dependency installation, reducing deployment complexity.
vs alternatives: Simpler than manual deployment because Docker handles environment setup; more portable than binary distribution because containers run consistently across platforms.
Registers the Firecrawl MCP server in the Smithery registry, enabling one-click installation and discovery through Smithery's MCP client marketplace. The server is published to Smithery with metadata (description, tags, configuration schema) allowing users to discover and install it without manual setup. Smithery handles server distribution, version management, and client integration.
Unique: Leverages Smithery's MCP server registry to enable one-click installation without manual configuration, reducing friction for end users. Smithery handles server discovery, versioning, and client integration, abstracting deployment complexity.
vs alternatives: More user-friendly than manual installation because Smithery handles discovery and setup; more discoverable than GitHub-only distribution because Smithery provides a centralized marketplace.
Supports connecting to self-hosted Firecrawl instances in addition to Firecrawl's cloud service through configurable API endpoint. The FIRECRAWL_API_URL environment variable allows operators to specify a custom Firecrawl endpoint, enabling deployment scenarios where Firecrawl runs on-premises or in a private cloud. The @mendable/firecrawl-js client library handles endpoint abstraction, routing all API calls to the configured endpoint.
Unique: Enables flexible deployment by supporting both cloud and self-hosted Firecrawl instances through simple endpoint configuration, allowing operators to choose deployment model without code changes. The endpoint abstraction is handled by @mendable/firecrawl-js, making self-hosted support transparent to MCP server code.
vs alternatives: More flexible than cloud-only solutions because self-hosted option is available; simpler than maintaining separate server implementations because endpoint configuration is unified.
Discovers all URLs within a website by crawling from a base URL and building a sitemap-like structure. The firecrawl_map tool accepts a base URL and optional parameters (max depth, include patterns, exclude patterns) and returns a hierarchical array of discovered URLs with metadata about page structure. Uses Firecrawl's crawler to traverse internal links up to specified depth, filtering by inclusion/exclusion patterns, and returns the complete URL graph without fetching full page content.
Unique: Provides lightweight URL discovery without content extraction, allowing agents to plan scraping strategy before committing credits to full content fetches. The depth-based crawling with pattern filtering enables selective discovery — agents can discover only URLs matching specific criteria (e.g., /blog/* paths) without exploring entire site.
vs alternatives: More efficient than scraping every page to build a sitemap because it skips content extraction; more reliable than parsing robots.txt or sitemaps.xml because it performs actual crawling and discovers dynamically-linked content.
Crawls an entire website and extracts content from all discovered pages in a single asynchronous operation. The firecrawl_crawl tool accepts a base URL and options (max pages, allowed domains, exclude patterns, scrape options) and returns a crawl ID for polling. The crawler discovers URLs, extracts markdown content from each page, and stores results server-side. Clients poll firecrawl_crawl_status to retrieve results as they complete, implementing an async job pattern rather than blocking until completion.
Unique: Implements server-side asynchronous crawling with job-based result retrieval, decoupling the crawl initiation from result consumption. The MCP server handles polling coordination through firecrawl_crawl_status, allowing AI agents to initiate long-running crawls and check progress without blocking. Firecrawl's backend manages the entire crawl lifecycle including URL discovery, content extraction, and result storage.
vs alternatives: More scalable than sequential scraping because crawling happens server-side in parallel; simpler than managing Puppeteer/Playwright browser pools because Firecrawl abstracts browser automation and handles rate limiting internally.
Polls the status of an in-progress or completed website crawl and retrieves extracted content. The firecrawl_crawl_status tool accepts a crawl ID and returns current progress (pages crawled, pages remaining, completion percentage), status state (running/completed/failed), and paginated results. Implements polling pattern where clients repeatedly call this tool with the same crawl ID to check progress and incrementally retrieve content as pages are processed, supporting streaming-like result consumption.
Unique: Provides non-blocking status and result retrieval for asynchronous crawls, enabling agents to manage long-running operations without blocking. The polling pattern with pagination allows incremental result consumption — agents can start processing results before the entire crawl completes, reducing end-to-end latency for large crawls.
vs alternatives: More flexible than blocking crawl operations because agents can check progress and retrieve partial results; simpler than webhook-based result delivery because polling requires no external infrastructure setup.
+5 more capabilities