| 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 the AniList MCP server for detailed information about anime and manga. It utilizes a structured API endpoint that supports GraphQL queries, enabling efficient data retrieval and minimizing over-fetching. The server is designed to handle complex queries, allowing developers to specify exactly what data they need, which optimizes performance and reduces latency.
Unique: Utilizes GraphQL for flexible and efficient querying, allowing clients to request exactly the data they need without excess payload.
vs alternatives: More efficient than REST APIs as it minimizes data transfer by allowing clients to specify their data requirements.
This capability enables applications to access and manage user profiles on AniList. It leverages the AniList MCP's user authentication and session management to securely retrieve and update user data. The integration supports various user-related queries, such as fetching watchlists, ratings, and personal notes, ensuring a seamless experience for users managing their anime and manga collections.
Unique: Incorporates secure user authentication mechanisms to ensure that user data is accessed and modified safely.
vs alternatives: Offers a more secure and robust user management system compared to traditional REST APIs, which may expose user data more easily.
This capability generates personalized anime recommendations based on user preferences and viewing history. It employs collaborative filtering algorithms and user data analysis to suggest titles that align with individual tastes. By integrating with the AniList database, it can access a wide array of user interactions and ratings to enhance the recommendation accuracy.
Unique: Utilizes collaborative filtering techniques tailored to anime data, leveraging extensive user interaction data from AniList for improved accuracy.
vs alternatives: More personalized than generic recommendation systems because it directly analyzes user preferences and viewing history.
This capability allows users to perform searches for anime and manga titles using various filters such as genre, release date, and popularity. It employs a full-text search engine integrated with the AniList database, enabling fast and relevant search results. The implementation supports fuzzy searching and ranking algorithms to ensure users find the most relevant content quickly.
Unique: Integrates a full-text search engine that supports advanced filtering and ranking for anime and manga, enhancing user search experiences.
vs alternatives: Offers faster and more relevant search results compared to traditional keyword-based search systems.
This capability enables the retrieval of detailed information about specific anime characters, including their backgrounds, abilities, and relationships. It uses a structured API call to access character data stored in the AniList database, allowing developers to enrich their applications with character-centric features. The implementation ensures that data is returned in a consistent format, facilitating easy integration into various applications.
Unique: Provides a dedicated endpoint for character data that ensures consistent and structured responses, making integration straightforward.
vs alternatives: More focused on character details than general anime APIs, which may not provide in-depth character information.
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 AniList MCP Server at 28/100.
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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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