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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches current and upcoming sports fixtures across multiple sports (football, basketball, tennis, esports) from Cloudbet's API, returning structured event data including teams, schedules, venues, and competition metadata. Implements polling-based synchronization with MCP server endpoints to expose fixture data as callable tools, enabling LLM agents to query live event calendars without direct API integration.
Unique: Exposes Cloudbet's fixture API as native MCP tools callable directly by Claude/LLMs without requiring developers to write custom API integration code — abstracts authentication and response parsing into standardized tool schemas
vs alternatives: Simpler than building custom REST wrappers because MCP handles tool registration and schema validation automatically; more specialized than generic sports APIs because it includes Cloudbet-specific stake limits and market metadata
Retrieves current betting odds, spreads, and market lines for active sports events from Cloudbet's live odds feed, structured by market type (moneyline, spread, over/under, prop bets). Implements MCP tool endpoints that parse Cloudbet's odds response format and expose odds as queryable data, allowing LLM agents to compare odds across markets and make data-driven betting recommendations.
Unique: Integrates Cloudbet's proprietary odds feed directly into MCP tool schema, allowing LLMs to query odds without understanding Cloudbet's REST API structure — includes automatic odds format normalization (decimal/fractional/implied probability)
vs alternatives: More accessible than raw Cloudbet API because MCP abstracts authentication and response parsing; more specialized than generic odds aggregators because it includes Cloudbet-specific stake limits and market restrictions
Queries Cloudbet's stake limit API to retrieve maximum bet amounts, minimum bet thresholds, and market-specific betting constraints for each fixture and market type. Implements MCP tool that returns constraint metadata, enabling LLM agents to validate bet sizes before placement and avoid rejected bets due to limit violations. Constraints are market-specific and may vary by user account tier.
Unique: Exposes Cloudbet's dynamic stake limit API as a queryable MCP tool, allowing LLM agents to enforce betting constraints programmatically without manual limit checking — includes account-tier-aware limit resolution
vs alternatives: More reliable than hardcoded bet limits because it queries live Cloudbet constraints; more granular than generic betting frameworks because it handles Cloudbet-specific tier-based limit variations
Combines fixture data, live odds, and stake limits into a unified MCP tool that generates structured betting recommendations by comparing odds across markets and calculating expected value. Implements decision logic that evaluates moneyline vs spread vs over/under markets for the same event, ranks recommendations by edge, and filters by stake constraints. Returns ranked recommendations with confidence scores and reasoning.
Unique: Synthesizes Cloudbet fixture, odds, and constraint data into a unified recommendation tool that LLMs can call once instead of making three separate API calls — includes built-in EV calculation and market comparison logic
vs alternatives: More efficient than calling individual odds/fixture tools because it combines data retrieval and analysis in one MCP call; more specialized than generic betting frameworks because it understands Cloudbet's market structure and constraints
Fetches esports-specific fixture and odds data from Cloudbet's esports coverage, including game titles (CS:GO, Dota 2, League of Legends), tournament names, team rosters, and esports-specific market types (map winner, round winner, first blood). Implements MCP tool that normalizes esports data structure and exposes it alongside traditional sports fixtures, enabling LLM agents to build unified sports/esports betting applications.
Unique: Exposes Cloudbet's esports data with game-specific market types (map winner, round winner) as native MCP tools, allowing LLMs to query esports markets using the same interface as traditional sports — includes esports-specific metadata normalization
vs alternatives: More integrated than separate esports APIs because it unifies esports and sports data in one MCP server; more specialized than generic sports APIs because it includes esports-specific market types and tournament structures
Registers Cloudbet API endpoints as standardized MCP tools with JSON schema definitions, enabling Claude and other LLM platforms to discover and call Cloudbet functions natively without custom integration code. Implements MCP protocol handlers that translate LLM tool calls into Cloudbet API requests, parse responses, and return structured JSON. Handles authentication, error handling, and response formatting transparently.
Unique: Implements full MCP protocol stack for Cloudbet, handling tool schema registration, LLM binding, authentication, and response formatting — eliminates need for developers to write custom API wrappers or authentication logic
vs alternatives: Simpler than building custom REST wrappers because MCP handles schema validation and tool discovery; more standardized than proprietary integrations because it uses the open MCP protocol
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 Cloudbet at 24/100.
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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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