| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Retrieves message history from Discord channels with full context including author, timestamps, and content. Implements Discord.py client integration to fetch messages from specified channels, supporting pagination through Discord's message API to retrieve historical message sequences. Works by establishing authenticated connection to Discord guild and querying channel message buffers.
Unique: Integrates Discord.py's native message fetching with MCP protocol, allowing LLM agents to directly query Discord message history without custom API wrappers or polling mechanisms
vs alternatives: Simpler than building custom Discord bot handlers because it exposes Discord.py's message API directly through MCP's standardized tool interface
Sends formatted text messages to specified Discord channels through authenticated bot connection. Implements Discord.py's send() method wrapped in MCP tool interface, supporting plain text and Discord markdown formatting (bold, italics, code blocks, embeds). Handles message validation and delivery confirmation through Discord's REST API.
Unique: Wraps Discord.py's message sending in MCP protocol, enabling LLM agents to post to Discord without managing bot connection state or handling Discord-specific formatting rules directly
vs alternatives: More reliable than webhook-based approaches because it uses authenticated bot connection with full permission context, avoiding webhook URL exposure and supporting richer message types
Adds or removes emoji reactions to Discord messages by message ID. Uses Discord.py's add_reaction() and remove_reaction() methods to modify message reactions through the Discord REST API. Supports both standard Unicode emojis and custom guild emojis, with validation against bot's reaction permissions.
Unique: Exposes Discord message reaction API through MCP, allowing agents to use reactions as lightweight state indicators without managing Discord client connection or emoji validation logic
vs alternatives: Simpler than building custom reaction handlers because MCP abstracts away Discord.py connection management and emoji validation, reducing boilerplate in agent code
Lists all guilds (Discord servers) the bot is a member of and enumerates channels within specified guilds. Implements Discord.py's guilds property and guild.channels iteration to fetch server metadata including names, IDs, member counts, and channel hierarchies. Returns structured data about server topology for navigation and permission checking.
Unique: Provides MCP-wrapped enumeration of Discord server topology, enabling agents to dynamically discover available channels and guilds without hardcoding channel IDs or server configurations
vs alternatives: More flexible than hardcoded channel lists because it discovers available servers and channels at runtime, supporting multi-server deployments without configuration changes
Lists members in a Discord guild and retrieves member details including roles, join dates, and permissions. Uses Discord.py's guild.members iteration and member object properties to fetch user metadata. Supports filtering and pagination for large servers with thousands of members.
Unique: Exposes Discord member enumeration through MCP with role and permission metadata, allowing agents to make access-control decisions based on server membership without custom permission checking logic
vs alternatives: More comprehensive than simple user lookups because it includes role hierarchy and permissions, enabling fine-grained access control in multi-role Discord communities
Implements MCP (Model Context Protocol) server that wraps Discord.py client, exposing Discord operations as standardized MCP tools. Handles MCP request/response serialization, tool schema definition, and error handling between LLM agents and Discord API. Manages bot connection lifecycle and authentication token handling.
Unique: Implements full MCP server wrapping Discord.py, standardizing Discord operations as MCP tools that work with any MCP-compatible LLM client without custom integration code
vs alternatives: More portable than custom Discord integrations because MCP standardization allows the same tool set to work across different LLM agents and frameworks without modification
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.
Discord MCP Server scores higher at 59/100 vs Supabase at 42/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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