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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to post tweets using natural language commands by parsing user input and translating it into the appropriate API calls for Twitter's v2 endpoint. It employs a command interpretation layer that maps user intents to specific tweet actions, ensuring that the posting process is streamlined and user-friendly. The integration with built-in rate limit handling ensures that users can post without exceeding Twitter's API constraints.
Unique: Utilizes a natural language processing layer specifically designed for interpreting tweet-related commands, making it easier for non-technical users to interact with Twitter.
vs alternatives: More intuitive than standard API wrappers, as it allows for direct natural language input without needing to format API requests manually.
This capability enables users to fetch Twitter profiles based on natural language queries. It uses a combination of intent recognition and API calls to retrieve user data from Twitter's v2 API. The system identifies keywords and phrases in user input to determine which profile to fetch, returning structured data that includes user details and recent tweets.
Unique: Incorporates intent recognition to streamline the profile fetching process, allowing users to query profiles in a conversational manner.
vs alternatives: More user-friendly than traditional API calls, as it allows for natural language queries instead of requiring precise API parameters.
This capability allows users to search for trending topics on Twitter using natural language queries. It leverages contextual understanding to interpret user input and translates it into the appropriate API requests to Twitter's trends endpoint. The system can filter results based on user-defined parameters, such as location or time frame, enhancing the relevance of the returned trends.
Unique: Employs contextual understanding to enhance the accuracy of trend searches, allowing for more relevant results based on user input.
vs alternatives: More adaptable than standard trend APIs, as it can interpret nuanced user queries for better results.
This capability automates the management of Twitter followers by allowing users to add or remove followers based on natural language commands. It integrates with Twitter's API to perform these actions while handling authentication and rate limits seamlessly. The system can also provide feedback on follower status and engagement metrics, enhancing user control over their Twitter presence.
Unique: Automates follower management through natural language commands, making it accessible for users without programming skills.
vs alternatives: More intuitive than traditional follower management tools, as it allows for conversational commands rather than manual API interactions.
This capability allows users to manage their Twitter bookmarks using natural language commands. It interprets user input to add, remove, or list bookmarks through the Twitter API. The system ensures that all actions are performed securely and efficiently, with built-in rate limit handling to prevent API overuse.
Unique: Enables bookmark management through natural language, making it easier for users to interact with their saved content without navigating the Twitter interface.
vs alternatives: More user-friendly than traditional bookmark management methods, as it allows for direct commands instead of requiring users to navigate through menus.
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 X Twitter Server at 29/100.
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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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