| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Retrieves search performance data from Google Search Console with pagination logic that aggregates up to 25,000 rows per query, compared to the standard Google API limit of 1,000 rows. Implements a service layer abstraction (SearchConsoleService) that wraps the Google Search Console API and handles multi-page result aggregation transparently, allowing AI assistants to analyze complete datasets without manual pagination or row-limit workarounds.
Unique: Implements transparent multi-page aggregation in the SearchConsoleService layer that automatically handles Google's 1,000-row pagination limit, returning up to 25,000 rows in a single logical request without requiring the client to manage pagination state or make multiple API calls
vs alternatives: Retrieves 25× more data per query than direct Google Search Console API access, eliminating the need for manual pagination loops or external ETL tools for complete dataset analysis
Applies regex pattern matching to filter search queries and URLs in analytics results, extending beyond Google Search Console's built-in basic operators. The SearchConsoleService layer intercepts raw API responses and applies client-side regex filtering before returning results, enabling complex pattern-based queries like 'all URLs matching /blog/[0-9]{4}/' or 'queries containing (buy|purchase|price)' without requiring manual post-processing.
Unique: Implements regex filtering as a post-processing layer in SearchConsoleService that operates on aggregated API results, allowing complex pattern matching without requiring separate API calls or external regex engines
vs alternatives: Enables regex-based filtering that Google Search Console's native UI and API do not support, allowing AI assistants to perform sophisticated query clustering and URL pattern analysis in a single request
Analyzes search analytics data to automatically identify SEO quick-win opportunities based on configurable thresholds for position, click-through rate (CTR), and impression count. The SearchConsoleService implements a Quick Wins detection algorithm that scores queries/URLs by their optimization potential (e.g., queries ranking 6-10 with high impressions but low CTR are high-priority targets for title/meta optimization), returning ranked recommendations without requiring manual threshold configuration.
Unique: Implements a built-in Quick Wins detection algorithm in SearchConsoleService that automatically scores and ranks optimization opportunities based on position, CTR, and impression thresholds, eliminating the need for external SEO tools or manual analysis workflows
vs alternatives: Provides automated opportunity prioritization directly within the MCP server, allowing AI assistants to generate actionable SEO recommendations without requiring integration with separate SEO analysis platforms or manual threshold configuration
Manages XML sitemap operations for registered Google Search Console properties, including submission of new sitemaps and retrieval of existing sitemap status. Implements three dedicated MCP tools that wrap Google Search Console's sitemap API endpoints, allowing AI assistants to submit sitemaps, list all submitted sitemaps, and retrieve detailed status information (indexed URLs, errors, warnings) for each sitemap without manual console navigation.
Unique: Provides three dedicated MCP tools (submit_sitemap, list_sitemaps, get_sitemap_status) that encapsulate Google Search Console's sitemap API endpoints with Zod schema validation, enabling programmatic sitemap management without direct API knowledge
vs alternatives: Enables automated sitemap management within AI assistant workflows, eliminating manual Google Search Console UI navigation and enabling integration with CI/CD pipelines for continuous indexing optimization
Inspects individual URLs to retrieve their current indexing status in Google Search Console, including whether the URL is indexed, any indexing errors, mobile usability issues, and rich result eligibility. Wraps Google's URL Inspection API through an MCP tool that accepts a URL and site property, returning detailed indexing metadata that helps diagnose why specific pages may not be indexed or appearing in search results.
Unique: Implements a single MCP tool that wraps Google's URL Inspection API with schema validation, providing structured access to detailed indexing metadata (coverage status, mobile usability, rich results) for individual URLs without requiring direct API integration
vs alternatives: Enables programmatic URL inspection within AI workflows, allowing automated indexing diagnostics and health checks without manual Google Search Console navigation or external SEO tools
Retrieves a list of all Google Search Console properties (sites) accessible to the authenticated service account, including site URLs, property types (domain or URL prefix), and verification status. Implements an MCP tool that calls Google's Search Console API to enumerate all properties, enabling AI assistants to discover available sites and select the appropriate property for subsequent operations without requiring manual property URL input.
Unique: Provides an MCP tool that enumerates all Search Console properties accessible to the service account, enabling dynamic property discovery without requiring hardcoded site URLs or manual property selection
vs alternatives: Allows AI agents to automatically discover and list available Search Console properties, enabling multi-site workflows and property selection without manual configuration or external tools
Validates all incoming MCP tool requests against Zod schemas before execution, ensuring type safety and preventing malformed requests from reaching Google APIs. The system defines schemas for each tool's input parameters (SearchAnalytics, SitemapSubmission, UrlInspection, etc.) in src/schemas.ts, with Zod providing runtime validation that generates JSON schemas for MCP protocol compliance and catches invalid inputs with detailed error messages.
Unique: Uses Zod schemas as the single source of truth for both runtime validation and JSON schema generation, eliminating schema duplication and ensuring MCP protocol compliance while providing detailed validation error messages
vs alternatives: Provides runtime validation with automatic JSON schema generation for MCP protocol, preventing invalid requests from reaching Google APIs and generating clear error messages without manual schema maintenance
Implements the Model Context Protocol (MCP) server using stdio-based communication, allowing any client (Claude Desktop, custom agents, other LLMs) to interact with the server through standard input/output streams. The MCP server in src/index.ts handles protocol-level request/response marshaling, tool registration, and stdio setup, enabling sandboxed execution and language-agnostic client integration without requiring HTTP servers or network configuration.
Unique: Implements MCP server using stdio-based communication with JSON-RPC 2.0 protocol, enabling sandboxed execution and language-agnostic client integration without HTTP servers or network exposure
vs alternatives: Provides sandboxed MCP integration that works with Claude Desktop and other MCP clients without requiring HTTP servers, network configuration, or cross-origin handling, simplifying deployment and security
+1 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.
Supabase scores higher at 42/100 vs Google Search Console at 22/100.
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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