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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables programmatic tweet posting with OAuth 2.0 authentication flow, supporting text content and media attachments through Twitter's v2 API with intelligent fallback to v1.1 endpoints when v2 media upload fails. Implements token refresh logic and secure credential management for long-lived authenticated sessions without requiring API keys to be stored in application code.
Unique: Implements dual-endpoint strategy with v2 API as primary and v1.1 fallback for media uploads, reducing failure rates when Twitter's v2 media endpoints are degraded. Handles OAuth token lifecycle (refresh, expiration, revocation) transparently within the MCP protocol layer rather than delegating to client.
vs alternatives: More resilient than direct REST API clients because fallback logic is server-side, and more secure than API-key-based approaches because OAuth tokens are short-lived and scoped to specific permissions
Processes image, video, and GIF attachments for tweet composition by validating formats, optimizing file sizes, and uploading through Twitter's v2 media endpoint with automatic chunked upload for large files. Falls back to v1.1 media upload endpoint if v2 fails, with format-specific handling (JPEG/PNG for images, MP4/MOV for video, GIF for animations).
Unique: Implements server-side format validation and chunked upload protocol for v2 API, with transparent fallback to v1.1 if v2 media endpoint fails. Handles media ID tracking and attachment to tweets within the MCP layer, abstracting away Twitter's separate media upload → tweet composition workflow.
vs alternatives: Simpler than raw Twitter API clients because media upload and tweet composition are unified in a single MCP tool call, and more reliable than naive implementations because it includes v1.1 fallback and handles chunked uploads for large files
Queries Twitter's v2 search API to retrieve tweets matching keyword, author, date range, and engagement criteria. Implements pagination through result sets, applies rate limiting per search tier, and returns structured tweet objects with metadata (author, engagement metrics, creation time, media references). Supports both recent tweets (last 7 days) and full-archive search depending on API tier.
Unique: Abstracts Twitter's v2 search API pagination and rate limiting within the MCP protocol, allowing agents to retrieve tweets without managing token state or rate limit headers. Supports both recent and full-archive search modes with automatic tier detection.
vs alternatives: More agent-friendly than raw API clients because pagination and rate limiting are handled transparently, and more flexible than pre-built search tools because it supports custom query syntax and field selection
Enables programmatic deletion of tweets owned by the authenticated user through Twitter's v2 delete endpoint. Validates that the authenticated user owns the tweet before deletion, handles authorization errors gracefully, and returns confirmation of successful deletion. Implements rate limiting per Twitter's delete API tier.
Unique: Implements authorization validation at the MCP server layer, preventing agents from attempting to delete tweets they don't own and returning clear error messages. Handles rate limiting transparently without exposing Twitter's rate limit headers to the agent.
vs alternatives: Safer than raw API clients because authorization is validated server-side, and more agent-friendly because rate limiting and error handling are abstracted away
Tracks rate limits across all Twitter API v2 endpoints (tweet creation, search, deletion, media upload) by parsing rate limit headers from responses and maintaining per-endpoint quota state. Implements backoff strategies (exponential backoff, retry-after headers) and prevents requests when quota is exhausted. Exposes rate limit status to agents for intelligent request scheduling.
Unique: Implements per-endpoint rate limit tracking within the MCP server, parsing Twitter's rate limit headers and maintaining quota state across multiple API calls. Exposes rate limit status to agents for proactive request scheduling rather than reactive error handling.
vs alternatives: More proactive than naive clients that retry after rate limit errors, and more transparent than libraries that hide rate limit details because agents can see quota consumption and plan requests accordingly
Maintains compatibility with both Twitter API v2 (primary) and v1.1 (fallback) endpoints, automatically selecting the appropriate version based on feature availability and endpoint health. Implements fallback logic for media uploads (v2 → v1.1) and graceful degradation when v2 endpoints are unavailable. Abstracts API version differences from agents through a unified interface.
Unique: Implements server-side API version negotiation and fallback, allowing agents to use v2 features without worrying about v1.1 compatibility. Automatically retries failed v2 requests with v1.1 endpoints for media uploads, reducing failure rates.
vs alternatives: More resilient than v2-only clients because it includes v1.1 fallback for media uploads, and more agent-friendly than clients requiring explicit API version selection because version negotiation is automatic
Implements the Model Context Protocol (MCP) server specification, exposing Twitter capabilities as standardized MCP tools with JSON schema definitions for inputs/outputs. Handles MCP protocol messages (tool calls, responses, errors) and integrates with MCP clients (Claude, custom agents) through stdio or HTTP transports. Provides structured error responses following MCP conventions.
Unique: Implements full MCP server specification with standardized tool schemas for all Twitter capabilities, enabling seamless integration with Claude and other MCP-compatible agents without custom protocol handling.
vs alternatives: More standardized than custom API integrations because it follows MCP conventions, and more agent-friendly than direct API clients because tool schemas are pre-defined and error handling is protocol-compliant
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) 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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