| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes a standardized MCP tool that lists all accessible channels in a connected Slack workspace by querying the Slack Web API's conversations.list endpoint. The server implements the MCP Tools primitive to surface this capability as a callable function with JSON-RPC transport, enabling LLM clients to discover and introspect channel metadata (name, topic, member count, archived status) without direct API knowledge. Uses Slack SDK for TypeScript to handle authentication via bot token and pagination of large channel lists.
Unique: Implements channel listing as a first-class MCP tool rather than a raw API wrapper, meaning the capability is discoverable and callable by any MCP-compatible client (Claude, custom agents) without requiring direct Slack SDK knowledge. Uses MCP's standardized tool schema to abstract away pagination and error handling.
vs alternatives: Simpler than building direct Slack API integrations because MCP handles transport, authentication context, and tool discovery; more discoverable than raw webhooks because the tool is self-describing in the MCP protocol.
Implements an MCP tool that fetches message history from a specified Slack channel using the conversations.history API, with optional thread-aware context retrieval via replies.list. The server accepts channel ID and optional timestamp range parameters, returning paginated message objects including user ID, timestamp, text content, and thread metadata. Handles both flat channel history and nested thread replies, enabling LLM clients to read conversation context before responding or analyzing.
Unique: Combines channel history and thread reply retrieval into a single MCP tool abstraction, handling the complexity of nested Slack conversation structure (flat messages vs threaded replies) transparently. The server manages pagination and scope validation, exposing a unified interface rather than requiring clients to call multiple Slack APIs.
vs alternatives: More context-aware than simple message fetching because it supports thread-aware retrieval; more efficient than raw Slack API calls because the MCP server can batch and cache common queries.
Exposes an MCP tool that posts messages to a Slack channel using the chat.postMessage API, supporting plain text, markdown, and Slack's Block Kit JSON formatting. The server accepts channel ID, message text, and optional parameters (thread timestamp for replies, metadata for custom fields, reply broadcast flag). Handles message composition, formatting validation, and returns the posted message timestamp for reference or threading.
Unique: Abstracts Slack's chat.postMessage API as an MCP tool, allowing LLM clients to post messages without handling authentication, formatting validation, or error recovery. Supports both simple text and complex Block Kit JSON, enabling rich UI composition from AI-generated content.
vs alternatives: More flexible than webhook-based posting because it supports threading, metadata, and dynamic formatting; more discoverable than raw API calls because the tool schema is self-describing in MCP.
Implements an MCP tool that posts replies to Slack threads using chat.postMessage with thread_ts parameter, enabling LLM clients to respond within conversation threads while optionally broadcasting replies to the channel. The server validates thread timestamp, handles reply-specific formatting, and manages the relationship between thread context and channel visibility. Supports both private thread replies and broadcast replies that appear in both thread and channel.
Unique: Treats thread replies as a first-class MCP capability separate from channel posting, recognizing that Slack's threading model requires explicit thread_ts handling. The server abstracts away the complexity of broadcast vs private replies, allowing clients to specify intent (thread-only or broadcast) without API-level details.
vs alternatives: More conversation-aware than generic message posting because it enforces thread context; simpler than managing thread state manually because the MCP server handles timestamp validation and broadcast logic.
Exposes MCP tools for adding and removing emoji reactions to Slack messages using the reactions.add and reactions.remove APIs. The server accepts channel ID, message timestamp, and emoji name, managing the relationship between message identity (channel + timestamp) and reaction state. Supports standard Slack emoji names and custom emoji, with error handling for invalid emoji or permission issues.
Unique: Implements reaction management as discrete MCP tools (add and remove) rather than a single toggle, giving clients explicit control over reaction state. The server validates emoji names and handles Slack's idempotent reaction semantics, where adding a duplicate reaction is a no-op.
vs alternatives: Lighter-weight than message posting for simple acknowledgments because reactions don't create notification noise; more discoverable than raw API calls because the tool schema is self-describing in MCP.
Implements the Model Context Protocol server-side transport layer using TypeScript SDK, exposing all Slack capabilities as standardized MCP Tools with JSON-RPC 2.0 communication. The server registers tool handlers, manages request/response serialization, and implements the MCP initialization handshake. Supports stdio and SSE (Server-Sent Events) transport mechanisms, allowing MCP clients (Claude, custom agents) to discover and invoke Slack tools through a unified protocol interface.
Unique: Implements the full MCP server lifecycle (initialization, tool registration, request handling) using the official TypeScript SDK, making it a reference implementation for how to expose third-party APIs as MCP tools. The server handles protocol-level concerns (schema validation, error serialization) transparently, allowing clients to treat Slack as a native capability.
vs alternatives: More standardized than custom API wrappers because it uses the official MCP protocol; more discoverable than direct Slack SDK usage because tools are self-describing; enables multi-client reuse because MCP is client-agnostic.
Manages Slack bot token authentication by accepting the token at server initialization (via environment variable or startup parameter) and using it for all subsequent API calls. The server stores the token securely in memory and passes it to the Slack SDK for each request, handling token validation and API authentication errors. Does not implement token refresh or rotation; assumes long-lived bot tokens.
Unique: Implements authentication at the MCP server level rather than delegating to clients, centralizing token management and reducing the risk of token exposure in client code. The server acts as a trusted intermediary, validating all requests against the stored token.
vs alternatives: More secure than client-side token management because the token never leaves the server; simpler than OAuth flows because it uses long-lived bot tokens; more centralized than per-request authentication because the token is managed once at startup.
Implements error handling for Slack API failures (rate limits, invalid channels, permission errors, network timeouts) by catching exceptions from the Slack SDK and translating them into JSON-RPC error responses. The server returns descriptive error messages to MCP clients, including error codes and context, enabling clients to implement retry logic or graceful degradation. Does not implement automatic retries or exponential backoff.
Unique: Translates Slack API errors into standardized JSON-RPC error responses, providing a consistent error interface across all tools. The server preserves Slack-specific error details in the error data field, allowing clients to implement sophisticated error handling without parsing error messages.
vs alternatives: More informative than silent failures because errors are explicitly returned; more standardized than raw Slack error responses because they conform to JSON-RPC; enables client-side retry logic because error codes are machine-readable.
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.
Slack MCP Server scores higher at 59/100 vs Supabase at 42/100.
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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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