Supabase ranks higher at 42/100 vs New Relic Observability Integration Server at 26/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | New Relic Observability Integration Server | Supabase |
|---|---|---|
| Type | Product | MCP Server |
| UnfragileRank | 26/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to query various metrics from New Relic's observability platform using a unified interface. It utilizes a model-context-protocol (MCP) architecture to facilitate seamless communication between the user interface and backend services, enabling dynamic retrieval of metrics based on user-defined parameters. This integration ensures that users can access real-time data without needing to switch between different tools or interfaces.
Unique: Employs a unified interface that abstracts the complexity of querying multiple data sources, making it user-friendly.
vs alternatives: More intuitive than traditional dashboards, allowing for quicker access to metrics without complex navigation.
This capability enables users to set up monitoring for their applications effortlessly through a guided interface. It leverages predefined templates and best practices to streamline the configuration process, ensuring that essential metrics and alerts are automatically included. This approach reduces the time and effort required to get applications monitored effectively.
Unique: Utilizes a template-based approach to simplify the monitoring setup process, making it accessible for users with varying levels of expertise.
vs alternatives: Faster setup than manual configurations, allowing teams to focus on development rather than monitoring logistics.
This capability provides users with tools to manage alerts effectively, including setting thresholds, notifications, and escalation policies. It integrates with New Relic's alerting framework, allowing users to define custom alert conditions based on their specific metrics and application needs. The system supports multi-channel notifications to ensure that the right team members are informed promptly.
Unique: Offers a highly customizable alert management system that integrates seamlessly with existing New Relic metrics, enhancing responsiveness.
vs alternatives: More flexible than basic alerting systems, allowing for tailored notifications based on specific application needs.
This capability allows users to explore and visualize infrastructure entities within the New Relic platform. It employs a graph-based representation of entities and their relationships, enabling users to understand dependencies and performance metrics at a glance. This feature is particularly useful for identifying bottlenecks and optimizing resource allocation.
Unique: Utilizes a graph-based model to represent infrastructure entities, providing a clear visual understanding of complex relationships.
vs alternatives: More intuitive than traditional list views, allowing for quicker identification of issues and dependencies.
This capability enables users to analyze observability data across various metrics and logs in a unified manner. It employs advanced querying capabilities and data visualization tools to help users derive insights from their observability data. The integration with New Relic's data processing engine allows for real-time analysis and reporting.
Unique: Combines metrics and logs analysis into a single workflow, leveraging New Relic's powerful data processing capabilities for comprehensive insights.
vs alternatives: More integrated than separate log and metric analysis tools, providing a holistic view of application performance.
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 New Relic Observability Integration Server at 26/100.
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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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