What can @sigmacomputing/slack-mcp-server do?

slack message sending via mcp protocol, slack channel and user lookup with context retrieval, slack conversation history retrieval with pagination, slack reaction and emoji interaction, slack thread management and replies, slack user and channel permission checking, mcp protocol translation and tool schema generation

@sigmacomputing/slack-mcp-server

MCP ServerFree

MCP server for interacting with Slack

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

slack message sending via mcp protocol

Medium confidence

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.

Solves for

Send notifications from an AI agent to a Slack channel when a task completesHave an LLM post formatted alerts or reports to team channels programmaticallyEnable a multi-step workflow where an agent notifies stakeholders via Slack after processing data

Best for

Teams building LLM agents that need to notify Slack channels

Developers integrating Slack notifications into MCP-based workflows

Non-technical teams using Claude or other MCP-compatible LLMs to automate Slack communications

Requires

Slack workspace with bot token (xoxb-*) with chat:write scope

MCP client compatible with tool-use-integration (Claude, Anthropic SDK, or other MCP-aware LLM)

Network access to Slack API endpoints (api.slack.com)

Limitations

Message formatting limited to Slack's block kit and markdown syntax — no custom HTML or rich media embedding beyond what Slack natively supports

No built-in rate limiting or queue management — rapid message sends may hit Slack API throttling (60 messages per minute per channel)

Requires valid Slack workspace token with chat:write scope — cannot send to channels the bot lacks permissions for

What makes it 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

slack channel and user lookup with context retrieval

Medium confidence

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.

Solves for

Resolve a channel name like 'engineering' to its actual channel ID before sending a messageLook up a user's timezone or status to determine if they're available before pinging themRetrieve a list of all channels or team members to help an agent decide where to post information

Best for

Agents that need to dynamically discover Slack channels or users at runtime

Workflows where the target channel/user is specified by name rather than hardcoded ID

Teams with large Slack workspaces where manual ID lookup is impractical

Requires

Slack bot token with users:read and conversations:read scopes

MCP client that can call multiple sequential tools (for lookup → message workflows)

Workspace with fewer than 10,000 channels/users for practical in-memory caching

Limitations

In-memory caching has no TTL — stale data if channels/users are created/deleted during server uptime without restart

Slack API rate limits apply per endpoint (15 requests per minute for users.list) — large workspaces may require pagination and multiple calls

Cannot retrieve private channel membership without explicit permissions — bot must be invited to private channels to list members

What makes it 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

slack conversation history retrieval with pagination

Medium confidence

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.

Solves for

Retrieve recent messages from a channel to give an LLM context about ongoing discussionsFetch a thread's full conversation history to analyze sentiment or extract decisions madeLoad the last N messages from a channel to provide context for a new agent response

Best for

Agents that need to understand recent team discussions before taking action

Workflows that analyze Slack conversations for insights (sentiment, decisions, action items)

Teams using Slack as a knowledge base and needing to surface relevant past discussions

Requires

Slack bot token with channels:history or groups:history scope (depending on channel type)

MCP client capable of handling paginated responses or iterating through cursors

Channel ID (not name) for reliable history retrieval

Limitations

Slack API limits history retrieval to the last 90 days for free workspaces — older messages require paid plan

Pagination cursor expires after 12 hours — long-running jobs fetching large histories may fail mid-pagination

Message content is plain text or markdown — rich formatting (blocks, attachments) are returned as JSON but not rendered

What makes it 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

slack reaction and emoji interaction

Medium confidence

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.

Solves for

Have an agent add a checkmark emoji to a message when a task is completedRemove a 'pending' emoji from a message once it's been processedUse emoji reactions as a lightweight voting or categorization mechanism for agent-generated insights

Best for

Agents that need to provide lightweight feedback or status updates via emoji

Workflows using emoji as a state machine (e.g., 'pending' → 'in-progress' → 'done')

Teams that prefer emoji reactions over verbose messages for high-volume notifications

Requires

Slack bot token with reactions:write scope

Valid message timestamp (ts) from the target message

Emoji name (without colons, e.g., 'thumbsup' not ':thumbsup:')

Limitations

Limited to emoji available in the workspace — custom emoji must be pre-created in Slack

Cannot add reactions to messages older than 30 days in some workspace configurations

Emoji reactions are visible to all channel members — no private or conditional reactions

What makes it 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)

slack thread management and replies

Medium confidence

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.

Solves for

Post a reply to a thread to continue a discussion or provide additional informationFetch all replies in a thread to understand the full context of a conversationHave an agent join an existing thread to answer questions or provide updates

Best for

Agents that need to participate in ongoing Slack discussions without creating new messages

Workflows that analyze threaded conversations for decision-making or context

Teams using threads to organize discussions and needing agents to contribute contextually

Requires

Slack bot token with chat:write scope

Parent message timestamp (thread_ts) to reply to

Channel ID where the thread exists

Limitations

Thread replies are only visible if the parent message is still in the channel — deleting the parent collapses the thread

Slack limits thread depth display to 1000 replies in the UI — API can retrieve more but client rendering may struggle

Cannot edit or delete replies after posting — only the original author can modify their message

What makes it 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

slack user and channel permission checking

Medium confidence

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.

Solves for

Check if the bot can send messages to a channel before attempting to postVerify that the bot has been invited to a private channel before trying to retrieve historyDetermine if the bot has permission to add reactions or manage threads in a specific channel

Best for

Agents that need to gracefully handle permission errors before attempting Slack operations

Workflows that dynamically select channels based on bot permissions

Teams with complex Slack permission models needing to validate access before operations

Requires

Slack bot token with auth:read scope

Channel ID to check permissions for

Knowledge of required scopes for the intended operation

Limitations

Permission checks are point-in-time — permissions can change between check and operation if admin modifies bot roles

Scope checking is static (based on token) — cannot detect runtime permission changes like channel removal

No granular permission model — only checks if bot is member and has required scopes, not specific message-level permissions

What makes it 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

mcp protocol translation and tool schema generation

Medium confidence

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.

Solves for

Enable Claude or other MCP-compatible LLMs to call Slack operations without custom integrationsProvide a standardized interface for Slack operations that works across different LLM frameworksAllow non-technical users to build Slack automation workflows using natural language with LLMs

Best for

Teams using Claude or other MCP-compatible LLMs that need Slack integration

Developers building multi-tool LLM agents that include Slack as one capability

Organizations standardizing on MCP for LLM integrations across multiple services

Requires

MCP-compatible client (Claude with MCP support, Anthropic SDK 0.7+, or other MCP framework)

Node.js 16+ runtime for the MCP server

Network connectivity between MCP client and server

Limitations

MCP protocol overhead adds ~50-100ms latency per tool call compared to direct SDK usage

Tool schemas must match Slack API parameters exactly — any Slack API changes require server updates

No built-in retry logic or exponential backoff — client must handle Slack API transient failures

What makes it 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

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @sigmacomputing/slack-mcp-server, ranked by overlap. Discovered automatically through the match graph.

MCP Server28

@sigmacomputing/slack-mcp-server

MCP server for interacting with Slack

slack channel and conversation retrieval via mcp resourcesslack message sending via mcp protocolslack user and workspace metadata lookupslack thread and reply management

4 shared capabilities

MCP Server23

Slack

** - Channel management and messaging capabilities. Now maintained by [Zencoder](https://github.com/zencoderai/slack-mcp-server)

slack conversation history retrieval with context windowingslack channel listing and metadata retrievalslack user lookup and profile retrievalmcp-compliant slack channel message posting

4 shared capabilities

MCP Server29

slack-relay-mcp

Code-execution-based Slack MCP tool — CLI + TypeScript API + Claude Code skill

message pagination and history retrieval with filteringmcp resource exposure for slack channels and messagesslack message relay via mcp protocolcli-based slack message sending and retrieval

4 shared capabilities

MCP Server42

slack-mcp-server

Model Context Protocol (MCP) server for Slack Workspaces. This integration supports both Stdio and SSE transports, proxy settings and does not require any permissions or bots being created or approved by Workspace admins

slack workspace message retrieval and search via mcpslack channel and user metadata exposure via mcp resourcesthread-aware message context retrievalslack message posting and thread reply via mcp tools

4 shared capabilities

MCP Server46

Slack MCP Server

Read and send Slack messages and manage channels via MCP.

slack message history retrieval with thread contextslack message posting with channel targeting

2 shared capabilities

Product26

HowsThisGoing

Streamline team workflows with AI insights and Slack...

slack channel conversation ingestion and indexing

1 shared capability

Best For

✓Teams building LLM agents that need to notify Slack channels
✓Developers integrating Slack notifications into MCP-based workflows
✓Non-technical teams using Claude or other MCP-compatible LLMs to automate Slack communications
✓Agents that need to dynamically discover Slack channels or users at runtime
✓Workflows where the target channel/user is specified by name rather than hardcoded ID
✓Teams with large Slack workspaces where manual ID lookup is impractical
✓Agents that need to understand recent team discussions before taking action
✓Workflows that analyze Slack conversations for insights (sentiment, decisions, action items)

Known Limitations

⚠Message formatting limited to Slack's block kit and markdown syntax — no custom HTML or rich media embedding beyond what Slack natively supports
⚠No built-in rate limiting or queue management — rapid message sends may hit Slack API throttling (60 messages per minute per channel)
⚠Requires valid Slack workspace token with chat:write scope — cannot send to channels the bot lacks permissions for
⚠No message editing or deletion support — sent messages are permanent unless manually removed
⚠In-memory caching has no TTL — stale data if channels/users are created/deleted during server uptime without restart
⚠Slack API rate limits apply per endpoint (15 requests per minute for users.list) — large workspaces may require pagination and multiple calls

Requirements

Slack workspace with bot token (xoxb-*) with chat:write scopeMCP client compatible with tool-use-integration (Claude, Anthropic SDK, or other MCP-aware LLM)Network access to Slack API endpoints (api.slack.com)Node.js 16+ runtime for the MCP server processSlack bot token with users:read and conversations:read scopesMCP client that can call multiple sequential tools (for lookup → message workflows)Workspace with fewer than 10,000 channels/users for practical in-memory cachingSlack bot token with channels:history or groups:history scope (depending on channel type)

Input / Output

Accepts: text (message content), string (channel ID or name), string (thread timestamp for threaded replies, optional), string (channel name or ID), string (user name, email, or ID), string (channel ID), integer (limit: number of messages to retrieve, 1-1000), string (cursor for pagination, optional), string (thread_ts for thread-specific history, optional), string (message timestamp), string (emoji name), string (thread_ts for the parent message), string (reply text content), string (operation type: 'send_message', 'read_history', 'add_reaction', etc.), MCP tool call requests with parameters matching Slack API schemas

Produces: structured JSON (message metadata: timestamp, channel, message_ts), error response with Slack API error code if delivery fails, structured JSON (channel metadata: id, name, topic, member_count, is_private), structured JSON (user metadata: id, name, email, real_name, timezone, status, is_bot), structured JSON array (messages with: text, user, ts, thread_ts, reactions, reply_count), string (next_cursor for pagination if more messages exist), boolean (success/failure), error response with Slack error code if reaction fails, structured JSON (reply metadata: ts, thread_ts, user, text), structured JSON array (all replies in thread with metadata), structured JSON (permission status: has_access, required_scopes, missing_scopes, reason), MCP tool response with structured JSON matching Slack API response format

UnfragileRank

Adoption40%(30% weight)

Quality16%(25% weight)

Ecosystem40%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

7 capabilities

Visit @sigmacomputing/slack-mcp-server→

Repository Details

Package Details

npm

Registry

0.1.1

Version

5,249

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MCP server for interacting with Slack

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Capabilities7 decomposed

slack message sending via mcp protocol

Medium confidence

Solves for

Best for

Teams building LLM agents that need to notify Slack channels

Developers integrating Slack notifications into MCP-based workflows

Non-technical teams using Claude or other MCP-compatible LLMs to automate Slack communications

Requires

Slack workspace with bot token (xoxb-*) with chat:write scope

MCP client compatible with tool-use-integration (Claude, Anthropic SDK, or other MCP-aware LLM)

Network access to Slack API endpoints (api.slack.com)

Limitations

Message formatting limited to Slack's block kit and markdown syntax — no custom HTML or rich media embedding beyond what Slack natively supports

No built-in rate limiting or queue management — rapid message sends may hit Slack API throttling (60 messages per minute per channel)

Requires valid Slack workspace token with chat:write scope — cannot send to channels the bot lacks permissions for

What makes it unique

vs alternatives

slack channel and user lookup with context retrieval

Medium confidence

Solves for

Best for

Agents that need to dynamically discover Slack channels or users at runtime

Workflows where the target channel/user is specified by name rather than hardcoded ID

Teams with large Slack workspaces where manual ID lookup is impractical

Requires

Slack bot token with users:read and conversations:read scopes

MCP client that can call multiple sequential tools (for lookup → message workflows)

Workspace with fewer than 10,000 channels/users for practical in-memory caching

Limitations

In-memory caching has no TTL — stale data if channels/users are created/deleted during server uptime without restart

Slack API rate limits apply per endpoint (15 requests per minute for users.list) — large workspaces may require pagination and multiple calls

Cannot retrieve private channel membership without explicit permissions — bot must be invited to private channels to list members

What makes it unique

vs alternatives

slack conversation history retrieval with pagination

Medium confidence

Solves for

Best for

Agents that need to understand recent team discussions before taking action

Workflows that analyze Slack conversations for insights (sentiment, decisions, action items)

Teams using Slack as a knowledge base and needing to surface relevant past discussions

Requires

Slack bot token with channels:history or groups:history scope (depending on channel type)

MCP client capable of handling paginated responses or iterating through cursors

Channel ID (not name) for reliable history retrieval

Limitations

Slack API limits history retrieval to the last 90 days for free workspaces — older messages require paid plan

Pagination cursor expires after 12 hours — long-running jobs fetching large histories may fail mid-pagination

Message content is plain text or markdown — rich formatting (blocks, attachments) are returned as JSON but not rendered

What makes it unique

vs alternatives

slack reaction and emoji interaction

Medium confidence

Solves for

Best for

Agents that need to provide lightweight feedback or status updates via emoji

Workflows using emoji as a state machine (e.g., 'pending' → 'in-progress' → 'done')

Teams that prefer emoji reactions over verbose messages for high-volume notifications

Requires

Slack bot token with reactions:write scope

Valid message timestamp (ts) from the target message

Emoji name (without colons, e.g., 'thumbsup' not ':thumbsup:')

Limitations

Limited to emoji available in the workspace — custom emoji must be pre-created in Slack

Cannot add reactions to messages older than 30 days in some workspace configurations

Emoji reactions are visible to all channel members — no private or conditional reactions

What makes it 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

slack thread management and replies

Medium confidence

Solves for

Best for

Agents that need to participate in ongoing Slack discussions without creating new messages

Workflows that analyze threaded conversations for decision-making or context

Teams using threads to organize discussions and needing agents to contribute contextually

Requires

Slack bot token with chat:write scope

Parent message timestamp (thread_ts) to reply to

Channel ID where the thread exists

Limitations

Thread replies are only visible if the parent message is still in the channel — deleting the parent collapses the thread

Slack limits thread depth display to 1000 replies in the UI — API can retrieve more but client rendering may struggle

Cannot edit or delete replies after posting — only the original author can modify their message

What makes it unique

vs alternatives

slack user and channel permission checking

Medium confidence

Solves for

Best for

Agents that need to gracefully handle permission errors before attempting Slack operations

Workflows that dynamically select channels based on bot permissions

Teams with complex Slack permission models needing to validate access before operations

Requires

Slack bot token with auth:read scope

Channel ID to check permissions for

Knowledge of required scopes for the intended operation

Limitations

Permission checks are point-in-time — permissions can change between check and operation if admin modifies bot roles

Scope checking is static (based on token) — cannot detect runtime permission changes like channel removal

No granular permission model — only checks if bot is member and has required scopes, not specific message-level permissions

What makes it 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

mcp protocol translation and tool schema generation

Medium confidence

Solves for

Best for

Teams using Claude or other MCP-compatible LLMs that need Slack integration

Developers building multi-tool LLM agents that include Slack as one capability

Organizations standardizing on MCP for LLM integrations across multiple services

Requires

MCP-compatible client (Claude with MCP support, Anthropic SDK 0.7+, or other MCP framework)

Node.js 16+ runtime for the MCP server

Network connectivity between MCP client and server

Limitations

MCP protocol overhead adds ~50-100ms latency per tool call compared to direct SDK usage

Tool schemas must match Slack API parameters exactly — any Slack API changes require server updates

No built-in retry logic or exponential backoff — client must handle Slack API transient failures

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@sigmacomputing/slack-mcp-server

Capabilities7 decomposed

slack message sending via mcp protocol

slack channel and user lookup with context retrieval

slack conversation history retrieval with pagination

slack reaction and emoji interaction

slack thread management and replies

slack user and channel permission checking

mcp protocol translation and tool schema generation

Related Artifactssharing capabilities

@sigmacomputing/slack-mcp-server

Slack

slack-relay-mcp

slack-mcp-server

Slack MCP Server

HowsThisGoing

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

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@sigmacomputing/slack-mcp-server

Capabilities7 decomposed

slack message sending via mcp protocol

slack channel and user lookup with context retrieval

slack conversation history retrieval with pagination

slack reaction and emoji interaction

slack thread management and replies

slack user and channel permission checking

mcp protocol translation and tool schema generation

Related Artifactssharing capabilities

@sigmacomputing/slack-mcp-server

Slack

slack-relay-mcp

slack-mcp-server

Slack MCP Server

HowsThisGoing

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @sigmacomputing/slack-mcp-server

Are you the builder of @sigmacomputing/slack-mcp-server?

Get the weekly brief

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