| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Manages GoLogin browser profile creation, configuration, and deletion through MCP server endpoints that translate natural language requests into GoLogin API calls. The MCP server acts as a bridge between Claude/AI conversations and the GoLogin REST API, handling profile state transitions (create → configure → launch → close) with automatic credential injection and fingerprint management.
Unique: Exposes GoLogin profile management as MCP tools callable from Claude conversations, eliminating need to switch between UI and AI — profiles can be created/configured entirely through chat with automatic fingerprint generation and proxy binding
vs alternatives: Unlike manual GoLogin UI or raw API scripts, this MCP integration allows non-technical users to manage complex multi-profile automation through natural language while maintaining full programmatic control
Generates and applies realistic browser fingerprints (user agent, screen resolution, timezone, language, WebGL parameters, canvas fingerprinting resistance) to GoLogin profiles via MCP tool calls. The server translates high-level fingerprint requests (e.g., 'Chrome on Windows 10 in Germany') into GoLogin's fingerprint schema, applying anti-detection techniques to evade bot detection.
Unique: Integrates GoLogin's fingerprint synthesis engine into MCP conversation flow, allowing AI agents to reason about and generate appropriate fingerprints for specific automation scenarios rather than requiring manual fingerprint selection
vs alternatives: Compared to raw GoLogin API, this MCP layer enables Claude to intelligently select fingerprints based on target site requirements and automation intent, reducing manual configuration overhead
Binds HTTP/HTTPS/SOCKS5 proxies to GoLogin profiles with automatic credential injection and protocol negotiation. The MCP server translates proxy configuration requests into GoLogin's proxy binding schema, supporting proxy rotation, failover, and per-profile proxy assignment without manual proxy manager setup.
Unique: Exposes GoLogin's proxy binding as MCP tools with automatic credential handling, allowing Claude to manage proxy assignment across profiles without exposing raw proxy credentials in conversation logs
vs alternatives: Unlike standalone proxy managers, this MCP integration ties proxy configuration directly to profile lifecycle, ensuring proxy is bound before profile launch and automatically cleaned up on profile deletion
Launches GoLogin browser profiles with applied fingerprints and proxies, returning connection details (WebSocket URL, port) for remote control via Puppeteer/Playwright. The MCP server handles profile startup orchestration, waits for browser readiness, and provides session tokens for subsequent automation commands.
Unique: Bridges GoLogin profile lifecycle with Puppeteer/Playwright automation by exposing launch/close operations as MCP tools, enabling Claude to orchestrate full browser automation workflows without manual daemon management
vs alternatives: Unlike raw GoLogin CLI, this MCP integration allows AI agents to reason about profile state and automatically handle launch/close sequencing as part of multi-step automation plans
Coordinates creation, configuration, and execution of multiple GoLogin profiles in sequence or parallel, with automatic resource allocation and cleanup. The MCP server provides batch tools for creating profile groups, applying consistent configurations, and launching profiles with dependency management.
Unique: Provides MCP tools for coordinating multiple profile operations with template-based configuration, allowing Claude to reason about and execute large-scale profile deployments without manual iteration
vs alternatives: Unlike sequential GoLogin API calls, this MCP layer enables batch operations with dependency tracking and automatic resource cleanup, reducing complexity of managing dozens of profiles
Saves and restores GoLogin profile configurations (fingerprint, proxy, cookies, local storage) to enable profile snapshots and recovery from failures. The MCP server provides export/import tools that serialize profile state to JSON, enabling version control and disaster recovery.
Unique: Serializes GoLogin profile configurations to portable JSON format, enabling version control integration and disaster recovery without relying on GoLogin cloud storage
vs alternatives: Unlike GoLogin's built-in profile backup, this MCP layer enables Git-based profile versioning and programmatic recovery as part of automation workflows
Provides MCP tools for diagnosing profile issues (fingerprint mismatches, proxy failures, browser crashes) through Claude conversations. The server exposes profile logs, network traces, and diagnostic commands that Claude can interpret and suggest fixes.
Unique: Exposes GoLogin diagnostic APIs as MCP tools that Claude can query and interpret, enabling conversational troubleshooting where Claude suggests fixes based on log analysis
vs alternatives: Unlike GoLogin's UI-based debugging, this MCP layer enables Claude to proactively diagnose issues and suggest fixes without manual log inspection
Provides MCP tools that bridge GoLogin profile management with Puppeteer, Playwright, and Selenium automation frameworks. The server handles profile launch, connection string generation, and cleanup, allowing automation scripts to use GoLogin profiles transparently.
Unique: Provides framework-agnostic MCP tools that abstract GoLogin profile launch details, allowing automation frameworks to use profiles without framework-specific GoLogin plugins
vs alternatives: Unlike framework-specific GoLogin plugins, this MCP approach works across multiple frameworks and allows Claude to orchestrate profile lifecycle independently of automation script
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 GoLogin MCP server at 21/100.
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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
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