Supabase ranks higher at 42/100 vs live-sports-scoreboard-api at 27/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | live-sports-scoreboard-api | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 27/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes real-time sports scoreboard data through the Model Context Protocol (MCP) standard, allowing Claude and other MCP-compatible clients to query live game scores, team statistics, and match results without direct API calls. Implements MCP resource and tool handlers that translate incoming requests into structured queries against a sports data backend, returning JSON-formatted game state with scores, timestamps, and team metadata.
Unique: Implements MCP protocol bindings specifically for sports scoreboards, enabling Claude and other LLMs to natively query live game data as a first-class MCP resource without requiring users to manage separate API keys or polling logic — the MCP abstraction layer handles context injection and response formatting automatically.
vs alternatives: Simpler than building custom Claude tools or plugins for sports data because it leverages the standardized MCP protocol, making it composable with other MCP servers and automatically compatible with any MCP-aware client without additional integration work.
Implements MCP resource handlers that expose scoreboard data as queryable endpoints, allowing clients to request specific games, teams, or leagues through a standardized resource URI pattern. The server registers resource handlers that parse incoming MCP requests, validate parameters, and return structured scoreboard data formatted according to MCP resource response schemas.
Unique: Exposes scoreboard data as first-class MCP resources rather than tools, meaning clients can discover and query them through MCP's resource introspection APIs — this enables dynamic schema discovery and composition without hardcoding endpoint knowledge.
vs alternatives: More discoverable and composable than REST APIs because MCP resources are self-describing and can be introspected by clients, reducing the need for external documentation or manual integration.
Registers MCP tools that allow clients to invoke parameterized queries for sports scores, such as 'get_game_score(team, date)' or 'get_standings(league)'. The server defines tool schemas with input validation, executes the queries against the sports data backend, and returns results in a format suitable for LLM processing and display.
Unique: Implements MCP tool calling specifically for sports queries, with schema-based input validation and LLM-friendly output formatting — Claude can invoke these tools naturally without knowing the underlying API structure, and the server handles parameter validation before querying the backend.
vs alternatives: More flexible than hardcoded resource endpoints because tools support parameterized queries and Claude's reasoning can select the right tool based on user intent, whereas resources require exact URI matching.
Provides mechanisms for clients to receive live score updates as games progress, likely through MCP's notification or subscription patterns. The server monitors upstream sports data sources for score changes and pushes updates to subscribed clients, enabling real-time scoreboard displays without polling.
Unique: Implements real-time score streaming through MCP's notification/subscription model, allowing clients to receive live updates without polling — the server maintains connections to upstream data sources and pushes changes to subscribed clients, reducing latency and server load compared to polling-based approaches.
vs alternatives: More efficient than polling-based score fetching because the server pushes updates only when scores change, reducing API calls and network traffic while providing lower-latency updates to clients.
Aggregates scoreboard data from multiple sports leagues (NFL, NBA, MLB, NHL, soccer, etc.) into a unified query interface, allowing clients to request scores across different sports without managing separate API connections. The server normalizes league-specific data formats into a common schema and routes queries to the appropriate backend provider.
Unique: Normalizes data from multiple sports leagues into a unified MCP interface, allowing clients to query across NFL, NBA, MLB, NHL, and soccer with a single tool/resource set — the server handles league-specific API differences and schema mapping transparently.
vs alternatives: More convenient than managing separate integrations for each sport because clients use a single query interface regardless of league, and the server abstracts away league-specific API quirks and data format differences.
Augments raw score data with team and player metadata such as logos, team colors, player names, statistics, and historical records. The server caches or fetches this metadata from upstream sources and includes it in score responses, enabling rich UI rendering and contextual information without additional client queries.
Unique: Automatically enriches score responses with team logos, colors, player stats, and historical records by caching metadata from upstream sources — clients receive complete context in a single response without additional queries, reducing round-trips and enabling richer UI rendering.
vs alternatives: More efficient than clients fetching metadata separately because the server includes it in score responses, reducing network round-trips and enabling Claude to provide contextual information without additional tool calls.
Provides queries for league standings, team statistics, and historical records beyond individual game scores. The server aggregates season-long data (wins/losses, point differentials, playoff seeding) and serves it through MCP tools/resources, enabling clients to ask questions about team rankings, playoff scenarios, and statistical comparisons.
Unique: Aggregates season-long standings and statistics into queryable MCP tools, allowing Claude to answer questions about team rankings, playoff seeding, and statistical comparisons without clients needing to fetch and compute standings themselves.
vs alternatives: More useful than individual game scores because standings provide context for understanding team performance and playoff implications, and the server handles aggregation logic so clients don't need to compute standings from raw game data.
Provides queries for upcoming games, schedules, and game times across leagues. The server fetches schedule data from upstream sources and allows clients to ask about future matchups, game times, and scheduling information without manual calendar management.
Unique: Exposes schedule data through MCP tools, allowing Claude to answer questions about upcoming games and game times without clients needing to fetch schedules separately — the server handles timezone conversion and schedule caching automatically.
vs alternatives: More convenient than clients checking a sports website because Claude can answer schedule questions conversationally, and the server handles timezone conversion so users get game times in their local timezone without manual adjustment.
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 live-sports-scoreboard-api at 27/100. live-sports-scoreboard-api leads on ecosystem, while Supabase is stronger on quality.
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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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