Firecrawl MCP Server ranks higher at 62/100 vs Supabase MCP Server at 60/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Supabase MCP Server | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 60/100 | 62/100 |
| Adoption | 1 | 1 |
| Quality | 1 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Supabase Management API and PostgREST API through the Model Context Protocol using a monorepo architecture with three distribution channels: hosted HTTP endpoint at mcp.supabase.com with OAuth 2.1 Dynamic Client Registration, npm package with three entry points (CLI binary, programmatic SDK, server factory), and local development via Supabase CLI at localhost:54321/mcp. Uses a platform abstraction layer that decouples transport mechanisms from tool implementations, enabling any MCP-compatible client (Claude Desktop, Cursor, VS Code Copilot, Windsurf) to connect without custom integrations.
Unique: Implements MCP protocol with platform abstraction layer that decouples transport mechanisms (HTTP, stdio, programmatic) from tool implementations, enabling single codebase to serve hosted, npm, and CLI distribution channels simultaneously. Uses feature groups configuration pattern to selectively enable tool categories (account, database, storage, edge functions, branching) per deployment context.
vs alternatives: Unlike custom Supabase integrations for each AI tool, MCP standardization allows any MCP-compatible client to connect without tool-specific code, reducing maintenance burden and enabling cross-tool consistency.
Executes arbitrary SQL queries against Supabase PostgreSQL databases through a dedicated PostgREST MCP server (@supabase/mcp-server-postgrest) that translates MCP tool calls into PostgREST HTTP requests. Supports parameterized queries to prevent SQL injection, result streaming for large datasets, and automatic schema introspection. The implementation uses a query builder pattern that constructs PostgREST-compatible request payloads, handling authentication via Supabase service role keys and managing connection pooling through the PostgREST proxy layer.
Unique: Separates PostgREST API access into dedicated @supabase/mcp-server-postgrest package, enabling independent versioning and deployment from Management API server. Uses PostgREST's native HTTP API rather than direct database drivers, providing automatic connection pooling, row-level security enforcement, and API-level access control without exposing raw database credentials to MCP clients.
vs alternatives: PostgREST abstraction provides row-level security and API-level access control without exposing database credentials, whereas direct database drivers would require managing connection secrets and RLS policies at the driver level.
Provides a comprehensive testing framework and mock system for developing and testing MCP tools without requiring live Supabase project access. The mock system includes mock implementations of Supabase APIs, tool registry mocking, and test utilities for verifying tool behavior. Enables developers to write unit tests for tools, test error handling and edge cases, and validate tool schemas without external dependencies. The testing framework integrates with standard Node.js testing tools and provides fixtures for common test scenarios.
Unique: Provides first-class testing framework and mock system as part of @supabase/mcp-utils package, enabling developers to test tools without external dependencies. Mock system includes realistic Supabase API response patterns and error scenarios for comprehensive test coverage.
vs alternatives: Integrated mock system enables fast, deterministic testing without external dependencies, whereas testing against live Supabase projects would be slow, non-deterministic, and require managing test data cleanup.
Implements OAuth 2.1 Dynamic Client Registration protocol for authenticating MCP clients connecting to the hosted HTTP endpoint at mcp.supabase.com/mcp. The authentication flow uses Dynamic Client Registration to dynamically register MCP clients without pre-configured credentials, issuing access tokens that authorize tool access. The implementation handles token validation, scope-based access control, and token refresh for long-lived connections. Supports both interactive OAuth flows for user-facing clients and service account flows for programmatic access.
Unique: Implements OAuth 2.1 Dynamic Client Registration for hosted endpoint, enabling secure multi-tenant access without pre-sharing credentials. Provides scope-based access control integrated with feature groups pattern for fine-grained capability management.
vs alternatives: OAuth 2.1 Dynamic Client Registration provides secure, standards-based authentication without pre-sharing credentials, whereas static API key authentication would require managing secrets for each client and lack fine-grained access control.
Supports three independent distribution channels (hosted HTTP endpoint, npm package, CLI via stdio) with unified tool implementations that work across all channels without modification. The platform abstraction layer handles transport-specific details (HTTP request/response, stdio message protocol, programmatic function calls) while tools implement business logic once. Each distribution channel can be deployed independently with different feature group configurations, authentication mechanisms, and operational requirements. The architecture enables users to choose the distribution channel that best fits their deployment model.
Unique: Implements unified tool implementations that work across three independent distribution channels (hosted HTTP, npm package, CLI) without modification, using platform abstraction layer to handle transport differences. Enables users to choose distribution channel based on deployment requirements without tool code changes.
vs alternatives: Multi-channel support with unified implementations reduces maintenance burden and enables flexible deployment, whereas channel-specific implementations would require duplicating tool logic and managing version compatibility across channels.
Provides a testing framework and mock API system that allows MCP server implementations to be tested without requiring live Supabase API credentials or network access. The mock system (@supabase/mcp-utils) includes predefined mock responses for common operations and allows custom mock configurations for testing edge cases. Tests can verify tool behavior, error handling, and response formatting without external dependencies.
Unique: Provides mock system integrated into @supabase/mcp-utils, allowing all MCP server implementations to share testing infrastructure. Mocks are TypeScript-based, enabling type-safe test configurations.
vs alternatives: Eliminates need for live API credentials in tests vs integration testing, while providing more realistic behavior than simple stub responses because mocks include error scenarios and edge cases
Manages Supabase database structure (create/drop/alter tables, columns, indexes, constraints) through the Supabase Management API, translated into MCP tool calls. Implements a schema-aware tool architecture that validates DDL operations before execution, provides schema introspection for understanding current database structure, and supports migration workflows. The platform abstraction layer handles API authentication and request construction, while tool implementations encapsulate domain logic for table creation, column modifications, and constraint management.
Unique: Implements schema management through Supabase Management API rather than direct SQL execution, providing API-level validation, audit logging, and integration with Supabase's branching system for preview databases. Tool architecture uses feature groups pattern to selectively enable schema operations per deployment context, preventing accidental schema modifications in production.
vs alternatives: Management API approach provides audit trails, integration with Supabase branching for safe schema testing, and API-level access control, whereas raw SQL execution would bypass these safeguards and require manual permission management.
Manages Supabase Auth users (create, list, update, delete, reset passwords) through the Supabase Management API, exposed as MCP tools. Implements account management tool group that handles user lifecycle operations, password resets, and user metadata updates. The implementation validates user input, enforces authentication policies, and integrates with Supabase's built-in auth system without requiring direct access to auth tables. Tool architecture includes safety checks to prevent unauthorized user modifications and audit logging for compliance.
Unique: Separates user management from application-level auth logic by using Supabase Management API, enabling AI assistants to perform admin-level user operations without exposing auth secrets or requiring direct database access. Implements safety checks and audit logging at the tool level to prevent unauthorized user modifications.
vs alternatives: Management API approach provides centralized user management with audit trails and rate limiting, whereas direct database manipulation would bypass Supabase's auth system safeguards and require managing auth secrets at the application level.
+6 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 MCP Server at 60/100.
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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