| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability enables users to access Kibana logs in real-time through a dedicated API that streams log data as it is generated. It employs WebSocket connections to maintain a persistent link, allowing for immediate updates and reducing latency in log retrieval. This approach is distinct as it supports both push and pull mechanisms for log data, enhancing responsiveness during debugging sessions.
Unique: Utilizes WebSocket connections for real-time data streaming, unlike traditional polling methods that can introduce latency.
vs alternatives: More efficient than traditional REST APIs for log access due to lower latency and real-time updates.
This capability allows users to perform queries on logs based on specific time ranges, leveraging Elasticsearch's powerful query DSL. It supports flexible date formats and can handle complex queries that filter logs by timestamps, enabling users to focus on relevant data during specific periods. This feature is implemented with a focus on optimizing query performance through indexing strategies.
Unique: Optimizes Elasticsearch's query capabilities with a focus on time-based filtering, enhancing performance for large datasets.
vs alternatives: More efficient than standard log querying tools due to its optimized indexing for time-based searches.
This capability provides a customizable authentication mechanism that can be easily integrated with various authentication providers. It supports OAuth, API keys, and basic auth, allowing teams to secure access to log data according to their specific needs. The implementation uses middleware to intercept requests and validate credentials before granting access to the log data.
Unique: Offers a modular authentication system that can be tailored to various enterprise security requirements, unlike rigid built-in options.
vs alternatives: More flexible than standard log access solutions, allowing for diverse authentication methods to fit organizational needs.
This capability integrates AI tools to analyze log data for patterns and anomalies, helping teams identify potential issues proactively. It uses machine learning models that can be trained on historical log data to recognize trends and flag unusual events. The integration is designed to work seamlessly with existing log data pipelines, enhancing the analytical capabilities without disrupting workflows.
Unique: Integrates AI models directly into the log analysis workflow, allowing for real-time anomaly detection without separate processing pipelines.
vs alternatives: More integrated than standalone AI log analysis tools, providing immediate insights within the existing log management framework.
This capability aggregates log data from multiple sources into a unified format, allowing for comprehensive analysis across different systems. It employs a microservices architecture where each service can independently collect and format logs before sending them to a central API. This design enables scalability and flexibility in managing diverse log sources.
Unique: Utilizes a microservices architecture for log aggregation, allowing independent scaling and management of log sources.
vs alternatives: More flexible than monolithic log aggregation solutions, enabling easier integration of new log sources.
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 Kibana Log Access Server at 32/100.
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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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