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Uses Slack's Web API under the hood with user-level authentication, abstracting API pagination and rate-limiting into MCP resource endpoints.","intents":["I want my AI agent to search Slack message history to answer questions about past discussions","I need to build a chatbot that can retrieve context from specific Slack channels without creating a bot user","I want to integrate Slack conversation data into an LLM reasoning loop for decision-making"],"best_for":["AI agents and LLM applications that need read-only access to Slack workspaces","Teams building internal tools that augment Slack with AI reasoning without bot management overhead","Developers integrating Slack as a knowledge source for RAG or context retrieval systems"],"limitations":["No bot token required means relying on user-level authentication, which may have stricter rate limits than bot tokens","Message retrieval is read-only; cannot modify or delete messages through this capability","Pagination and large result sets require manual handling by the client; no built-in streaming for massive message volumes","Slack API rate limits (typically 1 request per second for most endpoints) apply directly to query performance"],"requires":["Slack workspace with user account that has access to channels being queried","Slack user token or OAuth token with chat:read and search:read scopes","MCP client implementation (e.g., Claude Desktop, custom MCP host)","Network access to Slack API endpoints (api.slack.com)"],"input_types":["query string (for message search)","channel identifier (channel name or ID)","timestamp range (for filtering by date)","user identifier (for filtering by author)"],"output_types":["structured JSON with message metadata (timestamp, user, text, thread info)","message thread objects with nested replies","channel and user reference objects"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_1","uri":"capability://memory.knowledge.slack.channel.and.user.metadata.exposure.via.mcp.resources","name":"slack channel and user metadata exposure via mcp resources","description":"Provides structured access to Slack workspace metadata—channels, users, user groups, and their properties—through MCP resource endpoints, enabling AI agents to understand workspace topology and user context without making direct API calls. Caches metadata to reduce API calls and exposes it as queryable resources that MCP clients can introspect and reference during reasoning.","intents":["I want my AI agent to know which channels exist and their descriptions before searching messages","I need to resolve user mentions and IDs to actual user profiles for context enrichment","I want to build a Slack-aware agent that understands team structure and user groups"],"best_for":["LLM agents that need to understand Slack workspace structure before executing queries","AI assistants that need to resolve user/channel references and provide human-readable context","Teams building Slack-integrated workflows where understanding team topology is part of the reasoning process"],"limitations":["Metadata caching may become stale if workspace structure changes frequently; no real-time sync mechanism","User group and channel list endpoints have pagination limits; very large workspaces (10k+ channels) may require multiple requests","No access to private channel metadata unless the authenticated user is a member","User presence and status information is not included; only static profile data"],"requires":["Slack user token with users:read, channels:read, and usergroups:read scopes","MCP client capable of introspecting resource schemas","Workspace with at least basic channel and user setup"],"input_types":["channel ID or name (for channel lookup)","user ID or email (for user profile lookup)","user group ID (for group membership queries)"],"output_types":["channel objects with name, topic, description, member count, created timestamp","user profile objects with name, email, avatar, title, department","user group objects with members list and description"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_2","uri":"capability://tool.use.integration.slack.message.posting.and.thread.reply.via.mcp.tools","name":"slack message posting and thread reply via mcp tools","description":"Enables AI agents to post messages to Slack channels and reply in threads through MCP tool definitions, supporting formatted text, mentions, and thread context. Implements write operations as MCP tools (not resources) with validation and error handling, allowing agents to take actions in Slack as part of their reasoning workflow.","intents":["I want my AI agent to post summaries or alerts to a Slack channel as part of its workflow","I need an agent to reply in a thread with analysis or answers without human intervention","I want to build a Slack-integrated automation that sends notifications based on external events"],"best_for":["AI agents that need to communicate results or alerts back to Slack users","Automation workflows that use Slack as an output channel for notifications","Teams building AI assistants that participate in Slack conversations"],"limitations":["Message posting is asynchronous; no guarantee of delivery order if multiple posts are made in rapid succession","No support for rich formatting beyond basic Slack markdown (no custom blocks or interactive elements)","Cannot edit or delete messages after posting; only supports create operations","Rate limiting applies per user token; high-volume posting may hit Slack API throttles","No support for file uploads or attachments through this capability"],"requires":["Slack user token with chat:write scope","Target channel must be accessible to the authenticated user (public or user is a member)","MCP client that supports tool execution (not just resource reading)"],"input_types":["message text (plain text or Slack markdown)","channel ID or name (target for posting)","thread timestamp (for thread replies)","user mentions (as @user_id format)"],"output_types":["confirmation object with message timestamp and channel ID","error response with failure reason if posting fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_3","uri":"capability://tool.use.integration.dual.transport.mcp.server.with.stdio.and.sse.support","name":"dual-transport mcp server with stdio and sse support","description":"Provides both Stdio (standard input/output) and Server-Sent Events (SSE) transport implementations for the MCP protocol, allowing the server to be invoked either as a subprocess (Stdio) or as an HTTP endpoint (SSE). This dual-transport architecture enables flexible deployment: local tool integration via Stdio or remote/cloud deployment via SSE without code changes.","intents":["I want to run the Slack MCP server locally as a subprocess in Claude Desktop or another MCP client","I need to deploy the Slack MCP server as a remote HTTP service accessible from multiple clients","I want to choose between local and remote deployment based on my infrastructure without rewriting integration code"],"best_for":["Developers integrating Slack MCP into Claude Desktop or local MCP clients","Teams deploying MCP servers in cloud environments (AWS Lambda, Vercel, Docker containers)","Organizations that need both local development and remote production deployment options"],"limitations":["Stdio transport requires the MCP client to spawn a subprocess; not suitable for browser-based clients","SSE transport is unidirectional (server-to-client streaming); bidirectional communication requires a separate HTTP POST channel","SSE connections may be terminated by proxies or load balancers after idle periods; requires reconnection logic","Stdio transport inherits the process lifecycle of the parent; server crashes if parent process terminates","No built-in authentication for SSE endpoint; requires external API gateway or reverse proxy for security"],"requires":["Node.js 16+ (for Stdio transport)","Node.js 16+ with HTTP server capability (for SSE transport)","MCP client that supports either Stdio or SSE transport (e.g., Claude Desktop, custom MCP host)","For SSE: HTTP client capable of handling Server-Sent Events"],"input_types":["MCP protocol messages (JSON-RPC format)","HTTP POST requests (for SSE bidirectional communication)"],"output_types":["MCP protocol responses (JSON-RPC format)","Server-Sent Events stream (for SSE transport)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_4","uri":"capability://automation.workflow.proxy.configuration.and.network.resilience.for.slack.api.calls","name":"proxy configuration and network resilience for slack api calls","description":"Supports HTTP/HTTPS proxy configuration for outbound Slack API requests, enabling deployment in corporate networks with proxy requirements. Implements retry logic and connection pooling to handle transient failures and rate-limiting from Slack API, improving reliability in production environments.","intents":["I need to deploy the Slack MCP server behind a corporate proxy that intercepts outbound traffic","I want the server to automatically retry failed Slack API calls instead of failing immediately","I need reliable Slack integration that handles rate limits gracefully without crashing"],"best_for":["Enterprise teams deploying MCP servers in corporate networks with proxy requirements","Production deployments that need resilience against transient Slack API failures","Teams operating in environments with strict network policies and traffic inspection"],"limitations":["Proxy configuration is static; changing proxy settings requires server restart","Retry logic uses exponential backoff; very high-volume workloads may still exceed Slack rate limits","No circuit breaker pattern; if Slack API is down, retries will consume quota until timeout","Proxy authentication (username/password) must be embedded in configuration; no dynamic credential rotation","Connection pooling is per-process; horizontal scaling requires separate pool management per instance"],"requires":["Proxy server URL and credentials (if required) in environment variables or config file","Node.js HTTP agent configuration support (built-in to Node.js)","Slack API token with appropriate scopes"],"input_types":["proxy URL (http://proxy.example.com:8080)","proxy credentials (username:password)","Slack API requests (internally)"],"output_types":["successful Slack API responses (after retries if needed)","error responses with retry information"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_5","uri":"capability://tool.use.integration.no.permission.slack.integration.without.bot.installation","name":"no-permission slack integration without bot installation","description":"Operates entirely through user-level Slack authentication without requiring bot token creation, workspace admin approval, or formal bot installation. Uses the authenticated user's existing Slack permissions to access resources, eliminating the operational overhead of bot management while maintaining security through Slack's native permission model.","intents":["I want to integrate Slack with an AI agent without asking my workspace admin to approve a bot","I need a quick way to prototype Slack-powered AI features without formal bot setup","I want to avoid the complexity of managing bot tokens and permissions in my workspace"],"best_for":["Individual developers and small teams prototyping Slack AI integrations","Organizations with restrictive bot approval policies","Teams that want to minimize operational overhead for Slack integrations"],"limitations":["Permissions are tied to the authenticated user; if that user loses access, the integration breaks","Cannot access private channels unless the user is explicitly a member","User-level rate limits are typically stricter than bot-level limits (1 req/sec vs higher for bots)","No workspace-wide visibility; can only see channels and users the authenticated user can see","Presence and real-time events are not available; only historical data and on-demand queries"],"requires":["Slack user account with access to the workspace","Slack user token (generated via OAuth or legacy token)","User must have appropriate scopes granted (chat:read, search:read, users:read, etc.)"],"input_types":["Slack user token"],"output_types":["authenticated access to Slack resources the user can access"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_6","uri":"capability://memory.knowledge.mcp.resource.schema.exposure.for.agent.introspection","name":"mcp resource schema exposure for agent introspection","description":"Exposes all available Slack resources (messages, channels, users, threads) through standardized MCP resource schemas, allowing AI agents and LLM clients to introspect what data is available and how to query it. Implements JSON Schema definitions for each resource type, enabling agents to understand input/output types and constraints without external documentation.","intents":["I want my AI agent to discover what Slack data is available without reading documentation","I need the agent to understand the structure of Slack resources (channels, users, messages) automatically","I want to build agents that can reason about what Slack queries are possible based on available schemas"],"best_for":["AI agents and LLM systems that use schema introspection for planning and reasoning","Developers building generic MCP clients that need to understand server capabilities","Teams using Claude or other LLMs that leverage MCP schema information for better tool use"],"limitations":["Schema introspection adds latency to agent startup (one-time cost per session)","Complex nested schemas may be difficult for some LLMs to parse and reason about","Schema changes require server restart to propagate to connected clients","No versioning mechanism; schema evolution could break agents expecting older formats"],"requires":["MCP client that supports resource schema introspection","LLM or agent capable of parsing and reasoning about JSON Schema"],"input_types":["MCP schema introspection requests"],"output_types":["JSON Schema definitions for each resource type","resource list with descriptions and query parameters"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-slack-mcp-server__cap_7","uri":"capability://search.retrieval.thread.aware.message.context.retrieval","name":"thread-aware message context retrieval","description":"Retrieves messages with full thread context, including parent message, all replies, and metadata about thread participants. Implements thread traversal logic that reconstructs conversation threads from Slack's API responses, exposing complete thread trees to agents for reasoning about multi-turn conversations.","intents":["I want my AI agent to understand the full context of a Slack thread, not just individual messages","I need to retrieve a conversation thread and all its replies for analysis or summarization","I want to build an agent that can reason about discussion threads and provide informed responses"],"best_for":["AI agents that need to understand conversation context and thread structure","Summarization or analysis tools that operate on complete Slack conversations","Teams building Slack-integrated assistants that participate in threaded discussions"],"limitations":["Thread retrieval requires multiple API calls (one for parent, one for replies); adds latency","Very long threads (1000+ replies) may require pagination; not all replies may be retrieved in a single call","Thread metadata (participant count, last reply time) is computed from API responses; not always accurate for very active threads","Deleted messages in threads are not retrievable; gaps may appear in thread reconstruction","Slack API limits thread replies to 1000 most recent; older replies in very long threads are not accessible"],"requires":["Slack user token with chat:read scope","Thread timestamp (ts parameter) to identify the thread","Access to the channel containing the thread"],"input_types":["thread timestamp (ts)","channel ID","optional: limit on number of replies to retrieve"],"output_types":["thread object with parent message and nested replies array","message objects with user, timestamp, text, and thread metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Slack workspace with user account that has access to channels being queried","Slack user token or OAuth token with chat:read and search:read scopes","MCP client implementation (e.g., Claude Desktop, custom MCP host)","Network access to Slack API endpoints (api.slack.com)","Slack user token with users:read, channels:read, and usergroups:read scopes","MCP client capable of introspecting resource schemas","Workspace with at least basic channel and user setup","Slack user token with chat:write scope","Target channel must be accessible to the authenticated user (public or user is a member)","MCP client that supports tool execution (not just resource reading)"],"failure_modes":["No bot token required means relying on user-level authentication, which may have stricter rate limits than bot tokens","Message retrieval is read-only; 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