Supabase ranks higher at 42/100 vs @listo-ai/mcp-observability at 27/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @listo-ai/mcp-observability | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 27/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically intercepts and logs MCP tool calls with full context including tool name, arguments, execution time, and response payloads. Integrates at the MCP server protocol layer to capture invocations before they reach business logic, enabling observability without code instrumentation in tool handlers.
Unique: Operates at the MCP protocol layer rather than wrapping individual tool functions, capturing invocations uniformly across all tools without per-tool instrumentation boilerplate
vs alternatives: Lighter-weight than generic APM solutions because it understands MCP semantics natively, avoiding the overhead of HTTP-level tracing for tool calls
Captures inbound and outbound HTTP traffic with configurable payload sanitization rules that automatically redact sensitive fields (API keys, tokens, PII) before logging. Uses pattern-matching and field-name heuristics to identify and mask sensitive data without requiring manual annotation of every endpoint.
Unique: Implements automatic field-name heuristics (e.g., 'password', 'token', 'apiKey') combined with pattern matching to sanitize payloads without requiring explicit schema definitions for every endpoint
vs alternatives: More practical than manual annotation approaches because it catches common sensitive fields automatically; more flexible than fixed-schema solutions because rules can be customized per application
Provides a structured event emission API that allows developers to log domain-specific business events (e.g., 'user_signup', 'model_inference_completed') with typed metadata. Events are validated against optional schemas and enriched with automatic context (timestamps, user IDs, request IDs) before transmission to telemetry backends.
Unique: Combines structured schema validation with automatic context enrichment (timestamps, request IDs, user context), reducing boilerplate while maintaining data quality for analytics
vs alternatives: Lighter than full analytics platforms like Segment because it's SDK-based and doesn't require external infrastructure; more structured than raw logging because it enforces schema consistency
Captures user interactions in web applications (clicks, form submissions, navigation events) and emits them as structured telemetry events. Integrates with DOM event listeners and browser APIs to automatically track user behavior without requiring manual instrumentation of every interactive element.
Unique: Automatically captures DOM events without requiring manual instrumentation of each element, using event delegation and filtering to reduce noise while maintaining observability
vs alternatives: More lightweight than full session replay tools because it captures structured events rather than video; more practical than manual logging because it uses DOM event bubbling to instrument interactions automatically
Provides a pluggable backend interface that allows telemetry events to be routed to multiple destinations (e.g., Datadog, New Relic, custom HTTP endpoints, local file storage) without changing application code. Implements a provider registry pattern where backends are registered at initialization and events are fanned out to all active providers.
Unique: Uses a provider registry pattern that allows backends to be registered and unregistered at runtime, enabling dynamic telemetry routing without application restarts
vs alternatives: More flexible than single-backend solutions because it supports multi-destination routing; simpler than building custom event routing because the SDK handles provider lifecycle and event distribution
Automatically generates and propagates correlation IDs (trace IDs, request IDs) across MCP invocations, HTTP requests, and business events to enable end-to-end tracing. Uses async context (AsyncLocalStorage in Node.js) to maintain context across asynchronous boundaries without requiring explicit parameter passing.
Unique: Uses AsyncLocalStorage to maintain context across async boundaries automatically, eliminating the need to manually thread correlation IDs through function parameters
vs alternatives: Simpler than manual context propagation because it leverages Node.js async context primitives; more practical than external tracing systems because it works within a single process without requiring distributed tracing infrastructure
Automatically collects timing metrics for MCP tool invocations, HTTP requests, and custom code blocks, then aggregates them into percentiles, averages, and histograms. Metrics are computed in-process and included in telemetry events, enabling performance analysis without external metrics infrastructure.
Unique: Computes percentile metrics in-process using reservoir sampling, avoiding the need for external metrics backends while maintaining memory efficiency
vs alternatives: Lighter than Prometheus or Grafana because it doesn't require external infrastructure; more practical than manual timing because it automatically instruments common operations (HTTP, MCP tools)
Automatically captures uncaught exceptions and errors, including full stack traces, error context, and breadcrumb trails of preceding events. Integrates with global error handlers and promise rejection handlers to ensure errors are logged even if not explicitly caught by application code.
Unique: Integrates with global error handlers and promise rejection handlers to capture errors without requiring explicit instrumentation, while maintaining breadcrumb trails for debugging context
vs alternatives: More comprehensive than basic logging because it captures stack traces and event context automatically; simpler than Sentry because it's SDK-based and doesn't require external error tracking infrastructure
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 @listo-ai/mcp-observability at 27/100.
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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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