| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Retrieves raw message data from Discord channels and servers, converting Discord API responses into structured context that can be injected into MCP-compatible LLM clients. Uses Discord.py or similar library bindings to authenticate via bot token and fetch message history, metadata, and thread information, then normalizes the output into MCP resource format for seamless integration with Claude, other LLMs, or AI agents.
Unique: Implements Discord integration as a native MCP resource server rather than a generic API wrapper, allowing LLMs to treat Discord channels as first-class knowledge sources with automatic context normalization and MCP protocol compliance built-in
vs alternatives: Tighter integration than REST API wrappers because it speaks MCP natively, eliminating translation layers and enabling direct resource references in LLM prompts
Discovers and catalogs all accessible Discord channels, servers, and threads within a bot's permission scope, extracting metadata including channel type (text, voice, thread), topic, permissions, member count, and creation timestamps. Implements recursive traversal of guild hierarchies and category structures, normalizing channel relationships into a queryable resource tree that MCP clients can browse and reference.
Unique: Exposes Discord's hierarchical channel structure as queryable MCP resources with full metadata, enabling LLMs to understand and navigate server topology without separate API calls
vs alternatives: More discoverable than raw Discord.py because it pre-indexes and normalizes channel relationships, allowing LLMs to make informed decisions about which channels to query
Implements server-side message search across Discord channels using Discord's native search API or local indexing, supporting filters by author, date range, content keywords, and message type. Returns paginated result sets with relevance scoring, enabling LLMs to locate specific conversations or threads without loading entire channel histories. Handles Discord's search syntax (from:, before:, after:, has:) and translates them into MCP query parameters.
Unique: Wraps Discord's search API as an MCP tool, allowing LLMs to perform targeted searches as part of their reasoning loop rather than requiring pre-loaded context, reducing token overhead for large servers
vs alternatives: More efficient than loading full channel history because search happens server-side, returning only relevant messages and reducing context window pressure on the LLM
Enables MCP clients to send, edit, and delete messages in Discord channels through the MCP protocol, with support for embeds, attachments, and formatting. Implements Discord's message object schema (content, embeds, components) and handles rate limiting, permission validation, and error recovery. Allows LLMs to take actions in Discord (respond to conversations, post updates, correct mistakes) as part of agentic workflows.
Unique: Exposes Discord message posting as MCP tools with full schema validation and rate-limit awareness, allowing LLMs to take actions in Discord as part of structured tool-calling workflows rather than via unstructured API calls
vs alternatives: More reliable than direct API calls because it handles Discord's rate limiting and permission errors transparently, with automatic retry logic and clear error messages for LLM interpretation
Fetches user profiles, member information, and role assignments from Discord servers, including username, avatar, status, roles, join date, and custom metadata. Implements caching to reduce API calls and supports bulk member queries for large servers. Normalizes user data into a consistent schema that LLMs can reference when analyzing conversations or making decisions based on user context.
Unique: Caches user and member data to reduce Discord API calls, allowing LLMs to reference user context without triggering rate limits, and normalizes role information into queryable format
vs alternatives: More efficient than repeated API calls because it maintains a local cache of member data, enabling fast context lookups during LLM reasoning without blocking on Discord API latency
Manages message reactions and emoji interactions, allowing LLMs to add reactions to messages, retrieve reaction counts, and interpret emoji reactions as user feedback or voting signals. Supports both standard Unicode emojis and custom Discord server emojis, with caching of emoji metadata. Enables feedback loops where LLM responses can be validated through Discord reactions.
Unique: Treats emoji reactions as first-class feedback signals in the MCP protocol, enabling LLMs to both emit reactions (as responses) and interpret them (as user feedback), creating closed-loop interaction patterns
vs alternatives: More intuitive than text-based feedback because emoji reactions are native to Discord UX, allowing users to provide feedback without typing, and LLMs can interpret reactions as structured signals
Manages Discord threads (both public and private), enabling creation, retrieval, and message posting within threaded conversations. Implements thread metadata extraction (creator, creation time, archive status) and supports archiving/unarchiving threads. Allows LLMs to organize conversations into threads and maintain separate context for parallel discussions within a single channel.
Unique: Exposes Discord's native threading system as MCP tools, allowing LLMs to create and manage threads as a way to organize conversations and maintain separate context stacks for parallel discussions
vs alternatives: More scalable than flat message lists because threads provide natural conversation boundaries, reducing context window pressure and enabling LLMs to manage multiple parallel discussions in a single channel
Implements webhook-based event streaming from Discord to the MCP server, enabling real-time notification of messages, reactions, member joins, and other server events. Uses Discord's gateway connection or webhook payloads to push events to the MCP server, which can then trigger LLM actions or update context. Handles event deduplication and reconnection logic for reliable event delivery.
Unique: Implements Discord gateway streaming as an MCP resource, pushing events to the LLM client in real-time rather than requiring the LLM to poll for updates, enabling reactive agent patterns
vs alternatives: Lower latency than polling because events are pushed immediately via Discord gateway, enabling real-time agent responses without the overhead of repeated API calls
+1 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 raw-discord-mcp 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