| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability tracks player skills in real-time by integrating with the game's API to fetch and update skill levels dynamically. It employs a polling mechanism to regularly check for updates on player stats, ensuring that users have the most current information available. The architecture leverages WebSockets for efficient data transmission, allowing for instant updates without the need for constant page refreshes.
Unique: Utilizes WebSockets for real-time updates, unlike traditional polling methods that can be slower and less efficient.
vs alternatives: More responsive than competitors that rely solely on periodic polling for updates.
This capability generates dynamic leaderboards for various activities such as skills, bosses, and minigames by aggregating player data from the game's API. It uses a sorting algorithm to rank players based on their performance metrics, and caches results to optimize load times for users accessing the leaderboard frequently. The system is designed to handle large datasets efficiently, ensuring quick retrieval and display.
Unique: Incorporates caching to enhance performance, allowing for rapid leaderboard updates without excessive API calls.
vs alternatives: Faster leaderboard generation compared to other tools that do not utilize caching.
This capability allows users to compare multiple players' stats side by side by fetching their data simultaneously from the game's API. It employs a batch request strategy to minimize the number of API calls, improving efficiency and reducing latency. The comparison interface is designed to be user-friendly, displaying key metrics in an easily digestible format.
Unique: Utilizes batch requests to fetch data for multiple players at once, reducing the number of API calls and improving response time.
vs alternatives: More efficient than tools that require individual requests for each player.
This capability tracks player performance in various minigames by collecting data from the game's API and categorizing it based on different minigame types. It uses a modular architecture that allows for easy addition of new minigames as they are released, ensuring the tool remains up-to-date with the game's content. The data is processed to provide insights into player performance trends over time.
Unique: Modular design allows for easy updates and integration of new minigames, unlike static tools that require manual updates.
vs alternatives: More adaptable than competitors that do not support new minigames without significant rework.
This capability tracks player progress on clue scrolls by fetching relevant data from the game's API. It categorizes clues based on difficulty and type, allowing users to see their completion rates and remaining tasks. The system employs a user-friendly interface that visualizes progress, helping players strategize their clue scroll completion effectively.
Unique: Provides a visual progress tracking interface that enhances user engagement compared to text-based alternatives.
vs alternatives: More visually appealing and user-friendly than other tools that present data in a purely textual format.
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 osrs-stat 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
+1 more capabilities