| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability retrieves threat intelligence for any given IP address by querying a centralized database that aggregates data on malicious activities, geographical location, and Autonomous System Numbers (ASNs). It employs a microservices architecture to ensure scalability and quick response times, allowing users to access real-time data efficiently. The system integrates with various threat intelligence sources to provide comprehensive insights into the risk and reputation of IPs.
Unique: Utilizes a microservices architecture that allows for rapid querying and integration with multiple threat intelligence sources, ensuring up-to-date information.
vs alternatives: More comprehensive and faster than standalone IP lookup tools due to its integration with multiple threat intelligence databases.
This capability analyzes historical data related to malicious activities associated with an IP address by aggregating information from various threat intelligence feeds. It employs data normalization techniques to present a unified view of the IP's history, making it easier for users to identify patterns of behavior over time. This analysis is crucial for understanding the potential risks posed by an IP in current contexts.
Unique: Incorporates data normalization techniques to provide a coherent historical view of malicious activities, unlike many tools that only show isolated incidents.
vs alternatives: Offers a more detailed and structured historical analysis compared to basic IP lookup services.
This capability retrieves the geographical location and ASN for a given IP address by querying specialized geolocation databases. It combines IP address data with ASN information to provide users with insights into the network structure and geographical context of the IP. This is particularly useful for understanding the origin of traffic and potential jurisdictional issues.
Unique: Integrates multiple geolocation and ASN databases to provide a more accurate and comprehensive view than standalone services.
vs alternatives: Delivers more reliable geolocation and ASN information compared to basic IP lookup tools that may lack depth.
This capability evaluates the risk associated with an IP address by calculating a reputation score based on various factors, including historical malicious behavior, current threat intelligence, and user feedback. It employs machine learning algorithms to continuously improve the scoring model, ensuring that the risk assessments remain relevant and accurate over time.
Unique: Utilizes machine learning algorithms to dynamically assess risk and reputation, adapting to new data and trends more effectively than static scoring systems.
vs alternatives: Provides a more nuanced and adaptive risk assessment compared to traditional reputation scoring tools.
This capability assists incident response teams by providing contextual information about an IP address during investigations. It integrates with incident management systems to deliver real-time data on threats associated with the IP, enabling teams to make informed decisions quickly. This capability is designed to streamline the incident response workflow and improve overall efficiency.
Unique: Seamlessly integrates with existing incident management systems to provide contextual IP data, enhancing the speed and effectiveness of investigations.
vs alternatives: More efficient than manual data collection methods, allowing for quicker decision-making during incidents.
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 ip_search at 24/100. ip_search leads on ecosystem, while Supabase is stronger on quality.
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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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