| 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 detailed baseball statistics using a structured query language tailored for the MCP server. It employs a flexible schema that maps various data sources, such as Statcast and Fangraphs, to a unified API endpoint, enabling seamless data retrieval and integration. The implementation leverages a microservices architecture to ensure scalability and responsiveness, allowing for real-time data access and updates.
Unique: Utilizes a microservices architecture that allows for dynamic data source integration and real-time updates, unlike traditional monolithic APIs.
vs alternatives: More flexible and responsive than static REST APIs, allowing for real-time data access and integration with multiple sources.
This capability enables users to generate visualizations of baseball statistics directly from the queried data. It integrates with popular visualization libraries and frameworks, allowing for customizable charts and graphs based on user-defined parameters. The server processes the data and applies visualization templates, which can be adjusted dynamically to fit specific analytical needs.
Unique: Offers seamless integration with visualization libraries, allowing for real-time updates and customizability based on user input, which is often lacking in standard analytics tools.
vs alternatives: More interactive and customizable than static report generators, enabling real-time data visualization.
This capability allows for seamless integration with any MCP-compatible client, enabling developers to connect and interact with the MLB Stats Server easily. It supports a variety of protocols and data formats, ensuring that clients can communicate effectively with the server. The integration is designed to be lightweight and efficient, minimizing overhead and maximizing performance.
Unique: Designed specifically for MCP compatibility, ensuring that integration is straightforward and efficient, unlike many APIs that require extensive customization.
vs alternatives: Easier to integrate than many traditional APIs due to its adherence to the MCP standard.
This capability provides real-time updates of baseball statistics, allowing users to receive the latest data as it becomes available. The server employs WebSocket technology to push updates to connected clients, ensuring that users have immediate access to the most current information without needing to poll the server continuously.
Unique: Utilizes WebSocket technology for real-time data delivery, providing a more efficient and responsive experience compared to traditional polling methods.
vs alternatives: Faster and more efficient than REST APIs that require constant polling for updates.
This capability aggregates data from multiple sources, including Statcast, Fangraphs, and Baseball Reference, providing a comprehensive view of player and team statistics. The server normalizes and harmonizes data from these disparate sources, allowing users to query a unified dataset without needing to manage multiple API connections or data formats.
Unique: Offers a unified API for accessing multiple baseball data sources, reducing complexity and improving usability compared to managing separate APIs.
vs alternatives: More efficient than traditional methods that require separate API calls for each data source.
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.
MLB Stats Server scores higher at 45/100 vs Supabase at 42/100.
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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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