| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically discovers and displays the complete schema of connected MCP servers, including available tools, resources, and prompts with their input/output specifications. Uses the MCP protocol's introspection endpoints to fetch server capabilities and metadata without requiring manual documentation parsing or server-specific knowledge.
Unique: Provides real-time schema introspection directly via MCP protocol rather than requiring separate documentation or manual schema definition, enabling dynamic discovery of server capabilities at runtime
vs alternatives: More accurate than reading static documentation because it queries live server state, and faster than manual schema inspection because it automates the discovery process
Provides an interactive interface to call MCP server tools with custom parameters, execute them, and inspect their responses in real-time. Handles parameter validation against the tool's JSON schema, formats requests according to MCP protocol specifications, and displays structured responses with error handling and debugging information.
Unique: Combines schema-based parameter validation with live tool execution in a single interactive interface, eliminating the need to write separate test harnesses or manually construct MCP protocol messages
vs alternatives: Faster iteration than writing unit tests because it provides immediate feedback, and more reliable than curl-based testing because it handles MCP protocol details automatically
Enables browsing of resources exposed by MCP servers (files, documents, data objects) and retrieves their content through the MCP resource protocol. Displays resource hierarchies, metadata, and handles streaming or chunked content delivery for large resources, with support for filtering and searching resources by name or type.
Unique: Provides unified resource browsing across heterogeneous MCP servers through a consistent interface, abstracting away server-specific resource protocols and handling streaming/pagination transparently
vs alternatives: More flexible than direct file system access because it works with any MCP-compliant resource provider, and more discoverable than API documentation because resources are browsable in real-time
Displays available prompt templates from MCP servers, shows their parameters and descriptions, and allows executing prompts with custom arguments. Handles prompt variable substitution, formats prompt requests according to MCP specifications, and returns rendered prompt content or structured prompt responses for use in downstream applications.
Unique: Centralizes prompt template discovery and execution through MCP protocol, enabling version-controlled, server-managed prompt libraries that can be shared across multiple applications without duplication
vs alternatives: More maintainable than hardcoded prompts because templates are managed server-side, and more discoverable than scattered prompt files because they're exposed through a standard interface
Manages connections to MCP servers including establishing connections via stdio, HTTP, or SSE transports, monitoring connection health, handling reconnection logic, and gracefully shutting down connections. Provides connection status monitoring, error reporting, and automatic recovery from transient failures with configurable retry strategies.
Unique: Abstracts MCP transport details (stdio, HTTP, SSE) behind a unified connection interface with built-in health monitoring and automatic reconnection, eliminating transport-specific boilerplate in client applications
vs alternatives: More robust than manual connection handling because it includes automatic reconnection and health monitoring, and more flexible than hardcoded connections because it supports multiple transport types
Captures and displays all MCP protocol messages (requests, responses, notifications) flowing between client and server in real-time. Provides formatted message display with syntax highlighting, filtering by message type or direction, and detailed logging of protocol-level events including timing information, message sizes, and error details for debugging protocol compliance issues.
Unique: Provides transparent protocol-level message inspection without requiring server modifications or proxy setup, capturing the complete MCP message flow with timing and metadata for deep protocol analysis
vs alternatives: More detailed than application-level logging because it shows raw protocol messages, and easier to set up than network packet capture because it's built into the inspector
Collects performance metrics from MCP server interactions including tool execution time, resource retrieval latency, message round-trip time, and throughput statistics. Aggregates metrics over time, provides statistical summaries (min, max, average, percentiles), and identifies performance bottlenecks or slow operations for optimization analysis.
Unique: Automatically collects end-to-end performance metrics for all MCP operations without requiring manual instrumentation, providing statistical analysis and trend detection out of the box
vs alternatives: More comprehensive than manual timing because it tracks all operations automatically, and more accessible than APM tools because it's built into the inspector without external dependencies
Captures and analyzes errors from MCP server interactions, providing detailed error context including error type, message, stack traces, and the operation that triggered the error. Generates diagnostic reports with suggestions for resolution, categorizes errors by type (protocol, timeout, validation, server error), and tracks error patterns over time.
Unique: Provides intelligent error categorization and diagnostic suggestions specific to MCP protocol issues, going beyond raw error messages to help developers understand root causes and resolution paths
vs alternatives: More actionable than generic error logs because it provides MCP-specific context and suggestions, and more efficient than manual debugging because it automatically categorizes and analyzes error patterns
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 @mcp-use/inspector at 23/100. @mcp-use/inspector 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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