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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a bidirectional Model Context Protocol proxy that accepts stdio connections from local MCP clients (Claude Desktop, Cursor, etc.) and transparently forwards all tool calls over Streamable HTTP to a remote OP.GG MCP endpoint at https://mcp-api.op.gg/mcp. Uses StdioServerTransport for local consumer communication and StreamableHTTPClientTransport for outbound HTTP, with dynamic capability discovery at startup to mirror remote tools into the local server's tool registry without hardcoding tool definitions.
Unique: Uses dynamic capability discovery at startup (reads serverCapabilities from remote endpoint) and conditionally registers request/notification handlers on the local MCP Server, enabling the proxy to work with any remote MCP endpoint without hardcoding tool definitions. This contrasts with static tool registries that require rebuilding when upstream tools change.
vs alternatives: Simpler than building custom HTTP client integrations in each AI framework because it leverages standard MCP protocol, making it compatible with any stdio-based MCP client without modification.
Exposes 15 tools for querying League of Legends game data including champion statistics, summoner profiles, match history, resource guides, pro player stats, and esports schedules. Each tool accepts a desired_output_fields parameter to filter response payloads at the API level, reducing bandwidth and token consumption by excluding unnecessary fields. Tools are prefixed with 'lol_' and cover champion analytics, summoner rank/win rates, match timelines, and competitive esports data.
Unique: Implements field-level response filtering via desired_output_fields parameter, allowing clients to specify exactly which data fields to return. This reduces payload size by excluding unnecessary fields at the API level rather than post-processing, which is particularly valuable for token-constrained LLM contexts where every byte matters.
vs alternatives: More efficient than generic League APIs (Riot's official API, third-party REST endpoints) because it provides pre-computed meta analytics (win rates, pick rates, build recommendations) rather than raw match data, reducing the computation burden on the client side.
Supports three deployment methods: (1) Smithery configuration via smithery.yaml for automated process spawning, (2) manual npm/npx invocation (npx opgg-mcp or node dist/index.js), and (3) Docker deployment for containerized environments. All methods ultimately execute dist/index.js as a local stdio MCP server process. Enables flexible deployment across different environments (local development, CI/CD pipelines, containerized infrastructure) without code changes.
Unique: Supports three distinct deployment methods (Smithery, npm/npx, Docker) from a single codebase, enabling flexible deployment across different environments and use cases. This multi-method approach reduces friction for different deployment scenarios compared to single-method-only tools.
vs alternatives: More flexible than tools supporting only one deployment method because it accommodates Smithery-based orchestration, manual npm invocation, and containerized deployments without code changes.
Provides 6 tools for querying Teamfight Tactics (TFT) game data including meta deck compositions, item builds, augment recommendations, and play style classifications. Tools are prefixed with 'tft_' and return structured data about optimal team compositions for the current TFT set, itemization strategies, and augment synergies. Supports filtering by play style (e.g., 'aggressive', 'control', 'economy') and rank tier to surface meta-relevant recommendations.
Unique: Organizes meta data by play style (aggressive, control, economy) rather than just raw win rates, enabling AI agents to recommend compositions that match player preferences and game state. This contextual filtering is rarely exposed in generic TFT APIs, which typically return only statistical aggregates.
vs alternatives: Provides pre-computed meta compositions and augment synergies rather than requiring clients to aggregate raw match data, making it significantly faster for real-time coaching use cases compared to building custom analytics on top of raw TFT match APIs.
Exposes 6 tools for querying Valorant competitive data including agent statistics (pick rates, win rates, ban rates), map-specific meta, leaderboard rankings, and player match history. Tools are prefixed with 'valorant_' and support filtering by region (NA, EU, APAC, etc.) and rank tier to surface region-specific meta variations. Returns structured data about agent viability, map-specific strategies, and competitive player rankings.
Unique: Supports region-specific meta filtering (NA, EU, APAC, etc.), recognizing that Valorant competitive scenes have distinct agent preferences and strategies by region. This regional decomposition is rarely exposed in generic Valorant APIs, which typically aggregate global data.
vs alternatives: Provides pre-computed agent meta and leaderboard rankings rather than requiring clients to aggregate raw match data, making it significantly faster for competitive analysis compared to building custom analytics on top of raw Valorant match APIs.
Aggregates esports schedule, team roster, and tournament data across League of Legends, Teamfight Tactics, and Valorant competitive scenes. Returns structured data about upcoming matches, team information, player rosters, tournament brackets, and historical match results. Supports filtering by game title, region, and tournament tier (e.g., regional leagues, international events). Data is updated periodically as tournaments progress.
Unique: Aggregates esports data across three distinct games (League of Legends, TFT, Valorant) under a unified tool interface, allowing single queries to span multiple competitive scenes. Most esports APIs are game-specific; this unified approach reduces integration complexity for multi-game esports platforms.
vs alternatives: Consolidates esports schedules and rosters from multiple games into a single MCP interface, eliminating the need to integrate separate APIs for each game's esports data.
At startup, the proxy fetches serverCapabilities from the remote OP.GG MCP endpoint and dynamically registers corresponding request/notification handlers on the local MCP Server. This enables the proxy to work with any remote MCP endpoint without hardcoding tool definitions. When the remote endpoint adds, removes, or modifies tools, the local proxy automatically reflects these changes on the next startup without code changes. Implementation reads capabilities once at initialization and conditionally registers handlers based on what the remote server advertises.
Unique: Uses dynamic capability discovery at startup (reads serverCapabilities from remote endpoint) and conditionally registers handlers, eliminating the need for hardcoded tool definitions. This contrasts with static tool registries that require code changes when upstream tools change. Implementation in src/proxy-server.ts reads capabilities once and registers handlers based on what the remote server advertises.
vs alternatives: More maintainable than static tool registries because upstream tool changes are automatically reflected without proxy code modifications, reducing synchronization burden compared to manually-maintained tool definitions.
All 27 tools across League of Legends, Teamfight Tactics, and Valorant support a desired_output_fields parameter that filters response payloads at the remote API level. Clients specify which fields to include in the response (e.g., ['winRate', 'pickRate', 'banRate']), and the remote endpoint returns only those fields, reducing payload size and token consumption. This filtering happens server-side before the response is transmitted back through the proxy, minimizing bandwidth usage and LLM context overhead.
Unique: Implements server-side field filtering at the remote API boundary, allowing clients to specify exactly which response fields to include. This reduces payload size before transmission, contrasting with client-side filtering that requires transmitting the full response and then discarding unwanted fields.
vs alternatives: More efficient than client-side filtering because it reduces payload size at the source, saving bandwidth and token consumption compared to receiving full responses and filtering locally.
+3 more capabilities
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 OP.GG at 26/100.
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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
+1 more capabilities