Supabase ranks higher at 42/100 vs @sigmacomputing/slack-mcp-server at 31/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @sigmacomputing/slack-mcp-server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 31/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables LLM agents and MCP clients to send messages to Slack channels and direct messages through the Model Context Protocol, which abstracts Slack's Web API behind a standardized tool interface. The server translates MCP tool calls into authenticated Slack API requests, handling message formatting, channel resolution, and delivery confirmation without requiring clients to manage Slack SDK dependencies or authentication tokens directly.
Unique: Implements Slack messaging as a standardized MCP tool, allowing any MCP-compatible LLM (Claude, open-source models via Anthropic SDK) to send Slack messages without SDK boilerplate or token management in client code — the MCP server handles all authentication and API translation
vs alternatives: Simpler than building custom Slack integrations for each LLM framework because MCP standardizes the interface; more flexible than Slack Workflow Builder because it leverages LLM reasoning to decide when and what to send
Provides MCP clients with tools to search and resolve Slack channels and users by name or ID, returning metadata (channel topic, member count, user status, timezone) that enriches LLM context. The server queries Slack's conversations.list, users.list, and info endpoints, caching results in memory to reduce API calls and latency when agents need to identify targets for messages or gather team information.
Unique: Exposes Slack's conversations and users APIs as MCP tools with built-in in-memory caching and metadata enrichment, allowing LLMs to reason about team structure and availability without requiring agents to understand Slack API pagination or scope limitations
vs alternatives: More efficient than calling Slack API directly from LLM code because caching reduces redundant lookups; more contextual than simple ID-based routing because it returns metadata (timezone, status) that agents can use to make smarter decisions
Allows MCP clients to fetch message history from Slack channels or threads, returning messages with metadata (sender, timestamp, reactions, thread replies) in chronological order. The server implements pagination via Slack's conversations.history endpoint, supporting cursor-based iteration to handle channels with thousands of messages without loading all data into memory at once.
Unique: Wraps Slack's conversations.history API as an MCP tool with cursor-based pagination abstraction, allowing LLMs to iteratively load conversation context without managing pagination state or understanding Slack's rate limiting model
vs alternatives: More scalable than loading entire channel history at once because pagination prevents memory bloat; more LLM-friendly than raw Slack API because the MCP interface handles cursor management and returns structured message objects ready for analysis
Enables MCP clients to add or remove emoji reactions to Slack messages, allowing agents to acknowledge, categorize, or vote on messages programmatically. The server translates reaction requests into Slack's reactions.add and reactions.remove API calls, supporting any emoji available in the workspace and validating message timestamps to prevent errors.
Unique: Exposes Slack emoji reactions as MCP tools for add/remove operations, enabling agents to use emoji as a lightweight state indicator or feedback mechanism without requiring verbose message composition
vs alternatives: Faster and less noisy than posting status messages because emoji reactions don't clutter the conversation; more expressive than simple boolean flags because emoji can convey semantic meaning (checkmark = done, warning = needs attention)
Provides MCP clients with tools to post replies to message threads and retrieve thread metadata, enabling agents to participate in threaded conversations. The server uses Slack's chat.postMessage with thread_ts parameter to nest replies, and conversations.replies to fetch full thread context including all replies and their authors.
Unique: Abstracts Slack's thread_ts parameter and conversations.replies pagination as MCP tools, allowing agents to seamlessly participate in threaded conversations without understanding Slack's threading model or managing reply nesting
vs alternatives: More conversational than posting standalone messages because replies stay nested and don't clutter the main channel; more contextual than simple message sending because agents can read full thread history before replying
Enables MCP clients to verify whether the bot has required permissions to perform actions in specific channels or with specific users, returning permission status before attempting operations. The server checks bot membership, channel type (public/private), and required scopes against Slack's auth.test and conversations.info endpoints, preventing failed operations and providing early feedback to agents.
Unique: Provides pre-flight permission checking as an MCP tool, allowing agents to validate access before attempting operations and gracefully handle permission errors without trial-and-error API calls
vs alternatives: More robust than catching Slack API errors after the fact because it prevents failed operations; more efficient than repeatedly attempting operations because it validates permissions upfront
The core MCP server implementation translates Slack API operations into standardized MCP tool definitions with JSON schemas, allowing any MCP-compatible client (Claude, Anthropic SDK, open-source LLM frameworks) to discover and call Slack operations. The server implements the MCP specification for tool registration, parameter validation, and response formatting, abstracting Slack's REST API behind a unified tool interface.
Unique: Implements the full MCP server specification for Slack, providing standardized tool schemas and protocol handling that works with any MCP-compatible LLM without requiring custom client code or SDK integration
vs alternatives: More interoperable than Slack SDK integrations because MCP standardizes the interface across LLM frameworks; more maintainable than custom API wrappers because MCP tool schemas are self-documenting and discoverable
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 @sigmacomputing/slack-mcp-server at 31/100. @sigmacomputing/slack-mcp-server leads on adoption and ecosystem, while Supabase is stronger on quality.
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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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