| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Queries domain registrar databases and DNS systems to determine whether a domain name is currently available for registration. Implements WHOIS protocol queries and registrar API integrations to check availability status across multiple TLDs, returning immediate availability results with pricing information where available. The capability handles both generic TLDs (.com, .net, .org) and country-code TLDs through a unified query interface.
Unique: Implements MCP protocol integration for domain checking, allowing seamless embedding into AI agent workflows without custom API client code. Uses a unified abstraction layer over multiple registrar WHOIS endpoints and APIs, handling protocol differences transparently.
vs alternatives: Provides domain availability checking as an MCP tool that AI agents can call directly, whereas most domain APIs require custom HTTP client implementations and manual error handling.
Fetches and parses WHOIS records for registered domains, extracting structured information including registrant details, registrar information, nameservers, registration and expiration dates, and DNSSEC status. Implements intelligent parsing of WHOIS response text across different registrar formats (ICANN-compliant, regional variants, and proprietary formats) to normalize output into consistent structured data.
Unique: Provides WHOIS parsing as an MCP tool with automatic format detection and normalization across 50+ registrar response formats, eliminating the need for developers to implement custom WHOIS parsing logic.
vs alternatives: Handles WHOIS format variations automatically through intelligent parsing, whereas generic WHOIS clients return raw text requiring manual post-processing.
Processes multiple domain names in a single request, checking availability and retrieving WHOIS data for each domain while managing rate limits and request parallelization. Implements intelligent batching strategies that respect registrar rate limits (typically 50-200 queries/minute) and returns aggregated results with per-domain status, availability, and metadata in a single structured response.
Unique: Implements intelligent rate-limit-aware batching as an MCP tool, automatically parallelizing requests within registrar constraints and handling partial failures with transparent retry logic.
vs alternatives: Abstracts away rate limiting and batching complexity through MCP, whereas raw WHOIS APIs require developers to implement their own parallelization and backoff strategies.
Queries registrar pricing databases to retrieve current registration, renewal, and transfer costs for domains across different registrars and TLDs. Aggregates pricing from multiple registrars (GoDaddy, Namecheap, Google Domains, etc.) and returns comparative pricing data, identifying the cheapest options and highlighting premium domain pricing where applicable.
Unique: Aggregates pricing from multiple registrar APIs into a unified comparison interface, automatically handling currency conversion and promotional pricing variations across registrars.
vs alternatives: Provides multi-registrar pricing comparison as a single MCP tool call, whereas developers typically need to integrate with each registrar's API separately.
Performs DNS lookups and validation checks on domain configurations, including A/AAAA record resolution, MX record verification, NS record validation, and DNSSEC status checking. Returns detailed diagnostic information about DNS health, identifies misconfigurations, and flags potential issues like missing MX records or DNSSEC failures.
Unique: Provides comprehensive DNS validation as an MCP tool, combining multiple DNS query types (A, AAAA, MX, NS, DNSSEC) into a single diagnostic call with automatic issue detection and remediation suggestions.
vs alternatives: Integrates DNS diagnostics directly into AI agent workflows via MCP, whereas developers typically need to use separate DNS tools (dig, nslookup) and parse results manually.
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 Find-A-Domain at 17/100. Supabase also has a free tier, making it more accessible.
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