Supabase ranks higher at 42/100 vs Korean Stocks & Disclosures at 29/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Korean Stocks & Disclosures | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 29/100 | 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 and retrieve financial statements and disclosures for Korean companies based on specific dates. It employs a structured query mechanism that integrates with the underlying database of disclosures, ensuring that users can access accurate and timely financial data. The system is designed to handle multiple exchanges and formats, providing a comprehensive view of the financial landscape.
Unique: Utilizes a date-indexed database structure that allows for rapid retrieval of disclosures based on user-specified dates, enhancing the speed and accuracy of financial research.
vs alternatives: More efficient than generic financial data APIs due to its focus on Korean market disclosures and date-specific queries.
This capability enables users to obtain detailed stock profiles, including market classifications and trading data. It leverages an API integration that aggregates data from various exchanges, ensuring that users receive the most relevant and up-to-date information about Korean stocks. The profiles include metrics such as market cap, trading volume, and sector classification.
Unique: Incorporates a multi-exchange data aggregation approach, allowing for a unified view of stock profiles across different platforms, which is not commonly found in single-exchange focused tools.
vs alternatives: Offers a more holistic view of stock profiles compared to tools that focus on a single exchange.
This capability allows users to analyze historical trading data for Korean stocks, providing insights into price movements and trading volumes over time. It uses time-series analysis techniques to process and visualize data, enabling users to identify trends and patterns. The system can handle requests for specific date ranges and return insights in a user-friendly format.
Unique: Employs advanced time-series analysis algorithms to provide deeper insights into trading patterns, which are often overlooked by simpler data retrieval tools.
vs alternatives: Delivers more sophisticated analytical capabilities compared to standard trading data APIs.
This capability allows users to query and retrieve market classifications for various stocks, helping them understand the sectors and industries in which companies operate. It utilizes a classification schema that maps stocks to their respective markets, enabling users to filter and analyze stocks based on sector performance. The integration with exchange data ensures that classifications are up-to-date.
Unique: Utilizes a dynamic classification schema that adapts to changes in the market, ensuring users receive the most relevant sector information.
vs alternatives: More responsive to market changes compared to static classification databases.
This capability enables users to search and retrieve specific disclosure documents filed by companies, such as annual reports and regulatory filings. It employs a keyword-based search mechanism that indexes disclosure documents, allowing for efficient retrieval based on user queries. The documents are stored in a structured format to facilitate easy access and analysis.
Unique: Incorporates a robust indexing system for disclosure documents, allowing for rapid and accurate retrieval based on specific keywords, which is often lacking in traditional document retrieval systems.
vs alternatives: Faster and more efficient than generic document search tools due to its focus on financial disclosures.
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 Korean Stocks & Disclosures at 29/100.
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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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