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| Pricing | Paid | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts a company domain or email domain and returns enriched company metadata by querying Clearbit's proprietary database of 50M+ companies. Uses domain-to-company mapping with real-time verification against public data sources (SEC filings, Crunchbase, LinkedIn) and internal signals to validate and augment company attributes including industry, employee count, funding stage, and technology stack.
Unique: Combines proprietary web crawling, SEC/regulatory data ingestion, and third-party data partnerships (Crunchbase, LinkedIn) into a unified company graph with 50M+ entities, enabling single-API lookups vs. building custom multi-source aggregation pipelines
vs alternatives: Faster and more comprehensive than Hunter.io or RocketReach for company-level data because it indexes entire company profiles rather than just contact lists, reducing API calls needed per enrichment
Accepts an email address and returns enriched person metadata by reverse-matching against Clearbit's database of 500M+ professional profiles. Uses email-to-identity resolution with cross-referencing against LinkedIn, Twitter, GitHub, and other public sources to infer job title, company, location, social profiles, and professional interests. Includes confidence scoring to indicate data reliability.
Unique: Maintains a 500M+ person database indexed by email with continuous LinkedIn/social media scraping and deduplication logic to handle email address changes and job transitions, enabling single-API person lookups without requiring name or company context
vs alternatives: More comprehensive than Trumail or Verify Email because it returns full professional profiles (not just email validity), and faster than manual LinkedIn searches because matching is pre-computed against indexed profiles
Accepts CSV or JSON files containing hundreds to millions of records and processes enrichment asynchronously via job queues. Submits batch jobs to Clearbit's infrastructure, which distributes lookups across parallel workers, deduplicates requests, and returns results via webhook callbacks or polling. Includes rate-limiting, retry logic, and partial failure handling to ensure data consistency.
Unique: Implements distributed batch processing with deduplication across parallel workers, allowing single batch jobs to handle millions of records without duplicate API calls, combined with webhook-based result delivery for asynchronous integration into ETL pipelines
vs alternatives: More cost-effective than repeated real-time API calls for large datasets because deduplication and batching reduce total lookups; faster than sequential processing because parallel workers process records concurrently
Accepts an IP address and returns geolocation data (country, city, coordinates) plus inferred company information if the IP belongs to a corporate network. Uses IP-to-ASN mapping combined with Clearbit's company database to identify which company owns the IP block, enabling visitor identification without cookies or tracking pixels. Includes confidence scoring and privacy-safe fallback data.
Unique: Combines IP-to-ASN mapping with Clearbit's company database to infer corporate ownership of IP blocks, enabling company-level visitor identification without third-party tracking; includes privacy-safe fallback to geolocation-only data for non-corporate IPs
vs alternatives: More privacy-compliant than cookie-based visitor tracking because it uses only IP metadata; more accurate than MaxMind or IP2Location for company inference because it cross-references against Clearbit's 50M+ company database
Pushes enrichment data and company intelligence updates to customer-specified webhook endpoints in real-time as new data becomes available. Uses event-driven architecture where Clearbit's data pipeline triggers webhook events when company information changes (funding rounds, executive changes, technology stack updates). Includes retry logic, signature verification, and event deduplication to ensure reliable delivery.
Unique: Implements event-driven architecture where Clearbit's data pipeline triggers webhooks when company intelligence changes (funding, executives, tech stack), enabling real-time synchronization without polling; includes HMAC signature verification and built-in retry logic for reliable delivery
vs alternatives: More efficient than polling-based approaches because it only sends data when changes occur; more real-time than batch jobs because events are pushed immediately as data becomes available
Provides pre-built plugins for Salesforce, HubSpot, Pipedrive, and other CRMs that automatically enrich lead/contact records with Clearbit data without custom API integration. Plugins use CRM-native APIs (Salesforce REST API, HubSpot custom properties) to read contact/company records, call Clearbit enrichment endpoints, and write results back to CRM fields. Includes field mapping configuration and sync scheduling.
Unique: Provides pre-built, CRM-native plugins that use each platform's native APIs (Salesforce REST, HubSpot custom properties) for seamless integration without custom code, including UI-based field mapping and scheduled sync capabilities
vs alternatives: Faster to deploy than custom API integration because plugins are pre-configured for each CRM; more maintainable than Zapier/Make because it uses native CRM APIs rather than generic webhooks
Analyzes a company's website and digital footprint to detect installed technologies (web frameworks, analytics tools, hosting providers, payment processors) and infer firmographic attributes (company maturity, technical sophistication, growth trajectory). Uses web scraping, DNS analysis, and JavaScript fingerprinting to identify technology signals, then correlates with company metadata to build a technology profile. Returns structured technology inventory with confidence scores.
Unique: Combines web scraping, DNS analysis, and JavaScript fingerprinting to detect 500+ technologies across 20+ categories (web frameworks, analytics, hosting, payment processors), then correlates with company metadata to infer maturity and growth trajectory
vs alternatives: More comprehensive than Wappalyzer or BuiltWith because it correlates technology detection with company-level intelligence (funding, headcount, industry) to provide context; more accurate than manual research because detection is automated and continuously updated
Analyzes company behavior signals (website traffic patterns, hiring velocity, funding announcements, technology adoption) and assigns predictive intent scores indicating likelihood of purchase in the near term. Uses machine learning models trained on historical customer data to weight signals and generate 0-100 intent scores. Includes signal breakdown showing which factors contributed most to the score.
Unique: Uses machine learning models trained on historical customer conversion data to weight multiple signal types (hiring velocity, funding announcements, technology adoption, website traffic) into a single 0-100 intent score with signal attribution breakdown
vs alternatives: More comprehensive than simple signal detection because it combines multiple signals into a unified score; more actionable than raw signal lists because it prioritizes signals by predictive power
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 Clearbit at 19/100. Supabase also has a free tier, making it more accessible.
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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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