| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Performs web searches across three independent search engines (Bing, Brave, DuckDuckGo) with automatic cascading fallback when primary engines fail or return insufficient results. The system queries engines sequentially, aggregating results and applying quality assessment filters to ensure relevance before returning up to 10 ranked results. This architecture eliminates single points of failure inherent in API-dependent search solutions.
Unique: Implements direct scraping of three independent search engines with automatic cascading fallback rather than relying on a single paid API, eliminating API key requirements and single-point-of-failure risk. The architecture treats each engine as a redundant data source with quality assessment filters applied post-aggregation.
vs alternatives: Eliminates API costs and key management overhead compared to Serper/SerpAPI while providing better resilience than single-engine solutions like Tavily, though with slightly higher latency due to sequential fallback rather than parallel querying.
Extracts complete page content from multiple search result URLs concurrently using a two-tier strategy: fast HTTP requests with cheerio-based HTML parsing as primary method, automatically falling back to Playwright browser automation for JavaScript-heavy or dynamically-rendered pages. The system manages a pool of up to 3 browser instances with health checking to prevent resource exhaustion while maintaining extraction reliability across diverse page types.
Unique: Implements a dual-strategy extraction pipeline where HTTP+cheerio is the fast path for static content, with automatic Playwright fallback for dynamic pages, managed through a pooled browser instance system with health checks. This avoids the overhead of browser automation for 80%+ of pages while maintaining reliability for JavaScript-heavy sites.
vs alternatives: More efficient than browser-only solutions (Puppeteer, Playwright direct) due to HTTP-first strategy reducing browser overhead by ~70%, while more reliable than HTTP-only solutions by automatically handling JavaScript-rendered content without manual intervention.
Defines strict TypeScript types for all tool parameters, search results, and extracted content, with runtime schema validation to ensure MCP clients send correctly-formatted requests. The type system includes interfaces for search results, page content, extraction metadata, and configuration, enabling type-safe tool invocation and IDE autocomplete for client developers. Schema validation prevents malformed requests from reaching the extraction pipeline.
Unique: Defines strict TypeScript interfaces for all tool parameters and results with runtime schema validation, enabling type-safe tool invocation and IDE autocomplete for client developers. Validation prevents malformed requests from reaching the extraction pipeline.
vs alternatives: More type-safe than untyped JSON-RPC by enforcing parameter schemas at runtime, while simpler than full JSON Schema validation by using TypeScript interfaces. Enables IDE support and compile-time type checking for TypeScript clients.
Provides a performance-optimized search tool that returns only search engine snippets (titles, URLs, and brief descriptions) without extracting full page content. This tool uses the same multi-engine search infrastructure as the full-search capability but skips the content extraction pipeline entirely, reducing latency by 80-90% and eliminating browser resource consumption. Includes explicit browser cleanup to prevent resource leaks in long-running agent scenarios.
Unique: Separates search from content extraction as distinct MCP tools, allowing agents to choose between speed (snippets only) and comprehensiveness (full content) based on workflow requirements. Includes explicit browser cleanup to prevent resource leaks in long-running agent loops.
vs alternatives: Faster than full-search mode by 80-90% for agents that only need relevance assessment, while maintaining the same multi-engine resilience. More efficient than traditional search APIs for agents that need both quick and deep search capabilities in a single tool suite.
Extracts and returns the complete content from a single specified URL, applying the same dual-strategy extraction pipeline (HTTP+cheerio first, Playwright fallback) as the full-search tool but optimized for direct URL input rather than search results. Preserves page structure, metadata (title, description, author), and content formatting while filtering common boilerplate elements. Useful for agents that need to investigate specific URLs discovered through other means.
Unique: Provides a standalone extraction tool that accepts direct URLs rather than search queries, reusing the same dual-strategy extraction pipeline but optimized for single-page workflows. Preserves page metadata and structure while filtering boilerplate, enabling agents to investigate specific sources independently of search.
vs alternatives: More flexible than search-only tools for agents that need to investigate specific URLs, while maintaining the same extraction reliability as the full-search tool without requiring a search query first.
Manages a configurable pool of up to 3 Playwright browser instances with automatic health checking, graceful cleanup, and resource leak prevention. The pool implements queue-based request scheduling to prevent browser exhaustion, monitors instance health (detecting crashed or unresponsive browsers), and automatically restarts failed instances. This infrastructure enables concurrent content extraction across multiple pages while maintaining predictable resource consumption in long-running agent scenarios.
Unique: Implements a fixed-size browser pool (max 3 instances) with health checking and automatic restart logic, preventing resource exhaustion and memory leaks in long-running agent applications. The pool uses queue-based scheduling to handle concurrent requests without spawning unlimited browser processes.
vs alternatives: More efficient than spawning new browser instances per request (Puppeteer default) by reusing instances, while more reliable than unbounded pooling by enforcing strict limits and health checks. Prevents the memory leak and crash issues common in production web-scraping systems.
Applies configurable quality filters to search results after aggregation from multiple engines, assessing relevance based on query-to-result similarity, content length, and domain reputation heuristics. The system ranks results by relevance score and filters out low-quality matches before returning to the client. Quality thresholds are configurable via environment variables, allowing tuning for different use cases (strict filtering for research vs. permissive for exploration).
Unique: Applies post-aggregation quality filtering to multi-engine search results using configurable heuristics for relevance, content quality, and domain reputation. Allows tuning filter strictness via environment variables without code changes, enabling different quality profiles for different use cases.
vs alternatives: More transparent and configurable than opaque ranking algorithms used by commercial search APIs, while simpler to implement than machine learning-based quality assessment. Provides control over quality-vs-recall tradeoff through environment variable configuration.
Implements the Model Context Protocol (MCP) as a TypeScript server that communicates with MCP clients (Claude Desktop, LM Studio, custom implementations) via JSON-RPC over stdin/stdout. The server exposes three tools (full-web-search, get-web-search-summaries, get-single-web-page-content) as MCP resources with typed schemas, enabling seamless integration with any MCP-compatible client without custom integration code. Handles protocol versioning, error responses, and graceful shutdown.
Unique: Implements MCP as a standalone TypeScript server with stdio-based JSON-RPC, enabling integration with Claude Desktop and LM Studio without custom plugins or API wrappers. The server exposes three web search tools with typed schemas, allowing any MCP-compatible client to use web search as a native capability.
vs alternatives: More standardized than custom plugin APIs (Copilot, ChatGPT plugins) by using the open MCP protocol, while simpler to deploy than REST API servers by using stdio communication. Enables tool reuse across multiple LLM clients without reimplementation.
+3 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 Web Search MCP at 26/100.
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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