| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes live MLB game data through the Model Context Protocol (MCP) server interface, enabling Claude and other MCP-compatible clients to query current game scores, inning status, play-by-play events, and team statistics without direct API calls. Implements MCP resource and tool abstractions that translate MLB Gameday API responses into structured, LLM-consumable formats with automatic polling or event-driven updates.
Unique: Implements MCP server abstraction layer specifically for MLB Gameday API, allowing Claude and other MCP clients to access live baseball data through standardized protocol bindings rather than requiring direct API integration in each application
vs alternatives: Eliminates need for developers to implement MLB API authentication and polling logic themselves by providing MCP-native interface that works seamlessly with Claude and other MCP-compatible tools
Provides MCP resource endpoints that clients can subscribe to for streaming or polling live game events (plays, substitutions, score changes, game state transitions). Implements event filtering and formatting to deliver only relevant updates to the requesting client, reducing bandwidth and processing overhead compared to full game state polling.
Unique: Implements event filtering and subscription patterns within MCP resource model, allowing clients to subscribe to specific game events without receiving full game state updates on every poll
vs alternatives: More efficient than raw MLB API polling for event-driven use cases because filtering happens server-side before transmission, reducing client-side processing and bandwidth consumption
Exposes MCP tools that aggregate data across multiple concurrent MLB games, enabling queries like 'compare today's scores across all games' or 'find all games where the home team is winning by more than 5 runs'. Implements server-side aggregation logic that queries the MLB Gameday API for multiple games and returns structured comparison results without requiring the client to orchestrate multiple API calls.
Unique: Implements server-side aggregation and filtering logic within MCP tool definitions, allowing complex multi-game queries to be expressed as single tool calls rather than requiring client-side orchestration of multiple API requests
vs alternatives: Reduces client complexity and API call overhead compared to having Claude orchestrate multiple direct MLB API calls, by centralizing aggregation logic in the MCP server
Translates natural language questions from Claude into structured MLB Gameday API queries, then formats responses back into natural language suitable for LLM consumption. Implements query parsing logic that maps phrases like 'Who's winning?' or 'What inning are we in?' to appropriate API endpoints and parameters, with context awareness of previously queried games to avoid redundant API calls.
Unique: Bridges natural language input from Claude with structured MLB API queries by implementing context-aware query parsing that maintains game context across conversation turns, reducing the need for explicit game identifiers in follow-up questions
vs alternatives: More conversational than raw API access because it handles context and natural language interpretation, allowing users to ask follow-up questions without re-specifying game details
Provides MCP tools to query team and player statistics from the MLB Gameday API, including season-to-date stats, historical performance data, and comparative metrics. Implements caching or indexing of frequently-accessed statistics to reduce API call overhead, and formats results with contextual information (e.g., league rankings, historical comparisons) for LLM analysis.
Unique: Implements statistics lookup with optional caching and contextual enrichment (league rankings, historical comparisons), allowing Claude to answer statistical questions without requiring multiple API calls or external data aggregation
vs alternatives: More efficient than having Claude make individual API calls for each statistic because server-side caching and aggregation reduce redundant queries and provide pre-computed comparative metrics
Exposes MCP tools to query MLB schedule information, including upcoming games, past results, and fixture details (teams, start times, venues). Implements filtering by date range, team, or league, and returns structured schedule data suitable for calendar integration or game planning applications. Handles timezone conversion and daylight saving time adjustments automatically.
Unique: Implements automatic timezone conversion and daylight saving time handling within schedule queries, eliminating the need for client-side timezone logic and ensuring accurate start times across regions
vs alternatives: More reliable than raw API schedule data because it handles timezone complexity automatically, reducing errors in game time display and scheduling
Implements a fully-functional MCP server that exposes MLB Gameday data through standardized MCP resources and tools, enabling seamless integration with Claude and other MCP-compatible clients. Handles MCP protocol negotiation, resource discovery, tool registration, and request/response serialization without requiring client-side protocol implementation. Supports both resource-based (polling) and tool-based (function calling) access patterns.
Unique: Provides complete MCP server implementation for MLB data, handling all protocol-level concerns (negotiation, serialization, resource discovery) so developers can focus on data integration rather than protocol implementation
vs alternatives: Eliminates need for developers to implement MCP protocol themselves by providing a ready-to-deploy server that works with Claude and other MCP clients out of the box
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 mlb-gameday-bot at 25/100. mlb-gameday-bot leads on ecosystem, while Supabase is stronger on quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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