| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes 200+ web scraping and data extraction tools through the Model Context Protocol (MCP) standard, allowing AI agents and LLMs to discover and invoke scraping capabilities via a unified tool registry. Built on FastMCP framework, the server implements tool registration, schema validation (Zod), and request routing to Bright Data's backend infrastructure, enabling seamless integration with MCP-compatible clients (Claude Desktop, Cursor, Windsurf) through stdio transport without custom client implementations.
Unique: Implements MCP as the primary integration layer rather than REST APIs, enabling AI agents to discover and invoke 200+ scraping tools through a standardized protocol with automatic schema validation via Zod, eliminating custom client code for each tool
vs alternatives: Provides native MCP integration for AI agents (vs Bright Data REST API requiring custom HTTP clients), and standardizes tool discovery across all 200+ scrapers (vs point-to-point API integrations)
Automatically handles anti-bot detection, CAPTCHA bypass, and geographic restrictions by routing requests through Bright Data's Web Unlocker API, which manages proxy rotation, header spoofing, and JavaScript rendering transparently. The MCP server abstracts this complexity — agents invoke scraping tools without configuring proxies or handling detection logic; the backend automatically applies anti-detection strategies based on target domain fingerprinting and request patterns.
Unique: Abstracts anti-detection as a transparent backend service rather than requiring agents to manage proxies, headers, or detection evasion logic — the Web Unlocker API automatically applies domain-specific detection strategies based on fingerprinting without explicit agent configuration
vs alternatives: Eliminates manual proxy rotation and detection handling (vs raw proxy APIs), and provides domain-aware anti-detection strategies (vs generic proxy services with no bot-evasion logic)
Implements a modular architecture separating concerns into specialized tool modules (browser_tools.js, web_data_tools.js, general_scraping_tools.js), each handling a category of functionality. The central server.js orchestrator routes requests to appropriate modules, which implement tool-specific logic and return results. This modularity enables independent development, testing, and maintenance of tool categories, and allows selective tool loading based on configuration (e.g., disable browser tools if not needed).
Unique: Implements modular tool subsystem architecture with specialized modules for different tool categories (browser, web data, general scraping), enabling independent development and selective tool loading without modifying core server code
vs alternatives: Provides modular tool organization (vs monolithic tool registry), and enables selective tool loading (vs loading all tools regardless of need)
Enables AI agents to control headless Chrome browsers remotely through the Chrome DevTools Protocol (CDP), supporting session management, JavaScript execution, DOM interaction, and screenshot capture. The browser_tools.js subsystem manages browser lifecycle (launch, navigation, interaction), maintains session state across multiple tool invocations, and translates agent commands into CDP protocol messages, allowing agents to automate complex multi-step browser workflows without managing browser processes directly.
Unique: Implements CDP-based browser automation as an MCP tool, abstracting browser lifecycle management and session state — agents invoke high-level actions (navigate, click, screenshot) that are translated to CDP protocol messages, eliminating the need for agents to manage browser processes or protocol details
vs alternatives: Provides session-aware browser automation (vs stateless Playwright/Puppeteer APIs), and integrates browser control directly into MCP tool ecosystem (vs separate browser automation libraries requiring custom orchestration)
Provides 196+ dataset-specific scraping tools tailored to popular platforms (Amazon, LinkedIn, Google Maps, eBay, etc.), each implementing platform-specific parsing logic, pagination handling, and data normalization. Rather than generic HTML scraping, these tools understand platform structure and return normalized, structured data (products, profiles, reviews) with consistent schemas. The MCP server exposes each as a distinct tool with platform-specific parameters, allowing agents to extract data from major platforms without writing custom parsers.
Unique: Implements 196+ platform-specific parsers with normalized output schemas rather than generic HTML scrapers, allowing agents to extract structured data (products, profiles, reviews) from major platforms without writing custom parsing logic or understanding platform HTML structure
vs alternatives: Provides pre-built, maintained parsers for major platforms (vs building custom scrapers for each), and returns normalized schemas (vs raw HTML requiring post-processing)
Integrates search capabilities across multiple search engines (Google, Bing, Yandex) through dedicated MCP tools, allowing agents to perform web searches and retrieve ranked results without managing search engine APIs directly. Each search tool handles provider-specific parameters, result parsing, and pagination, returning normalized search results with title, URL, snippet, and ranking metadata. The integration abstracts provider differences, enabling agents to switch search engines or aggregate results across providers.
Unique: Abstracts multiple search engine APIs (Google, Bing, Yandex) behind a unified MCP tool interface with normalized result schemas, allowing agents to perform searches without managing provider-specific APIs or result parsing
vs alternatives: Provides multi-provider search abstraction (vs single-provider APIs like Google Custom Search), and normalizes results across providers (vs raw search engine responses with different schemas)
Implements token-based authentication for Bright Data services through environment variables (API_TOKEN), with optional zone configuration for Web Unlocker (WEB_UNLOCKER_ZONE) and Browser API (BROWSER_ZONE). The server validates tokens at startup and per-request, routing authenticated requests to appropriate Bright Data infrastructure zones. Zone configuration allows teams to use separate quotas, rate limits, and proxy pools for different use cases (e.g., dedicated zone for production scraping vs development testing).
Unique: Implements zone-based authentication allowing teams to partition quotas and proxy pools per use case (production vs development, different scraping types) through environment variables, enabling multi-tenant deployments without code changes
vs alternatives: Provides zone-level quota isolation (vs single shared quota), and supports environment-based configuration (vs hardcoded credentials)
Implements configurable rate limiting through the RATE_LIMIT environment variable (format: limit/time+unit, e.g., '100/1m' for 100 requests per minute), throttling tool invocations to prevent quota exhaustion and API abuse. The server enforces limits at the request level, queuing excess requests and returning rate-limit metadata (remaining quota, reset time) to agents, allowing them to implement backoff strategies or prioritize requests.
Unique: Implements configurable per-server rate limiting with queue-based request throttling, allowing teams to enforce quota constraints without external rate-limiting services, and exposing rate-limit metadata to agents for intelligent backoff
vs alternatives: Provides built-in rate limiting (vs external rate-limit services), and exposes limit status to agents (vs silent failures when quota exceeded)
+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 Bright Data 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