| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows the AI agent to send messages in Twitch chat automatically using the Twitch API. It leverages the broadcaster's access token to authenticate and send messages, ensuring that the agent operates under the broadcaster's identity. The implementation uses a simple command structure to format messages and can respond to chat events in real-time, enhancing viewer engagement.
Unique: Utilizes the Twitch API's chat message endpoint with a focus on real-time event handling, allowing for dynamic interaction.
vs alternatives: More responsive than traditional chatbots due to direct integration with Twitch's event-driven architecture.
This capability enables the AI agent to create and manage polls and predictions within Twitch streams. It interacts with the Twitch API to set up new polls, gather responses, and display results, all while maintaining user engagement. The implementation uses a structured approach to define poll parameters and manage state, ensuring that the polls are interactive and timely.
Unique: Integrates seamlessly with Twitch's polling system, allowing for real-time updates and results display during live streams.
vs alternatives: Offers a more streamlined experience compared to manual poll setups, reducing the time needed to engage viewers.
This capability allows the AI agent to create video clips from Twitch streams automatically. By utilizing the Twitch API's clip creation endpoint, it can generate clips based on predefined criteria such as highlights or viewer requests. The implementation includes a mechanism to specify clip duration and title, ensuring that clips are relevant and engaging.
Unique: Automates the clip creation process by integrating with Twitch's API, allowing for dynamic highlight generation based on stream activity.
vs alternatives: Faster than manual clipping methods, enabling real-time content generation without interrupting the stream.
This capability provides insights into chat activity during Twitch streams by analyzing message frequency, user engagement, and sentiment. It utilizes natural language processing techniques to assess chat messages and generate reports that help streamers understand viewer behavior. The implementation includes data aggregation and visualization components to present findings in an accessible format.
Unique: Employs advanced NLP techniques to provide deeper insights into viewer sentiment and engagement trends within Twitch chat.
vs alternatives: More comprehensive than basic chat logs, offering actionable insights that can inform content strategy.
This capability automates user moderation tasks on Twitch by leveraging the Twitch API to manage user permissions, timeouts, and bans. It uses predefined rules and AI-driven suggestions to determine appropriate actions based on user behavior in chat. The implementation includes a feedback loop that learns from moderation outcomes to improve future decisions.
Unique: Incorporates AI-driven suggestions for moderation actions, allowing for more nuanced and context-aware user management.
vs alternatives: More adaptive than traditional moderation bots, learning from past interactions to improve effectiveness.
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 Twitch-Streamer-Server at 32/100.
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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