| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to perform advanced search queries programmatically via a standardized protocol. It utilizes a flexible filtering system that can be customized based on user-defined parameters, enabling tailored search results. The integration with existing SearXNG instances is facilitated through multiple transport modes, including HTTP and stdio, ensuring compatibility and ease of deployment in various environments.
Unique: Utilizes a standardized protocol for meta-search, allowing seamless integration with various transport modes, unlike many search APIs that are rigid.
vs alternatives: More flexible than traditional search APIs due to its meta-search capability and customizable filtering options.
This capability enables users to specify various output formats for search results, such as JSON, XML, or plain text. By leveraging a modular design, it allows for easy extension of output formats without altering the core search functionality. This flexibility is particularly useful for developers who need to integrate search results into different systems or applications that require specific data formats.
Unique: The modular design allows for easy addition of new output formats, unlike many static search APIs that limit output options.
vs alternatives: More customizable than standard search APIs, which often provide a fixed output format.
This capability provides users with advanced filtering options to refine search results based on various criteria such as date, source, and content type. It employs a query-building approach that allows developers to construct complex queries with multiple filters, enhancing the precision of search results. This is particularly beneficial for applications that require highly relevant and context-specific information.
Unique: Offers a sophisticated query-building approach that allows for intricate filtering, unlike simpler search APIs that may only support basic keyword searches.
vs alternatives: Provides more nuanced filtering options compared to traditional search engines that often lack advanced query capabilities.
This capability allows SearXNG to be integrated with various systems through multiple transport modes, including HTTP and standard input/output (stdio). This design choice enables developers to choose the most suitable transport method for their environment, facilitating easier integration into diverse applications and workflows. It supports both web-based and command-line interfaces, enhancing accessibility.
Unique: Supports multiple transport modes, allowing for versatile integration options that are not commonly found in many search APIs.
vs alternatives: More adaptable than most search solutions that typically only support HTTP or RESTful interfaces.
This capability ensures that all search queries and results are handled in a privacy-respecting manner, meaning no user data is tracked or stored. It employs a decentralized architecture that allows users to perform searches without compromising their privacy, making it distinct from traditional search engines that often collect user data. This is crucial for applications focused on user confidentiality and data protection.
Unique: Utilizes a decentralized architecture that inherently respects user privacy, unlike centralized search engines that track user behavior.
vs alternatives: More privacy-centric than traditional search engines that often compromise user confidentiality.
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 SearXNG Search Server at 29/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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