What can slack-mcp-server do?

slack workspace message retrieval and search via mcp, slack channel and user metadata exposure via mcp resources, slack message posting and thread reply via mcp tools, dual-transport mcp server with stdio and sse support, proxy configuration and network resilience for slack api calls, no-permission slack integration without bot installation, mcp resource schema exposure for agent introspection, thread-aware message context retrieval

slack-mcp-server

MCP ServerFree

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

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

slack workspace message retrieval and search via mcp

Medium confidence

Exposes Slack workspace message history and search functionality through the Model Context Protocol, allowing AI agents and LLM-powered tools to query messages, threads, and conversation context without requiring bot token permissions or workspace admin approval. Uses Slack's Web API under the hood with user-level authentication, abstracting API pagination and rate-limiting into MCP resource endpoints.

Solves for

I want my AI agent to search Slack message history to answer questions about past discussionsI need to build a chatbot that can retrieve context from specific Slack channels without creating a bot userI 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

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)

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

What makes it unique

Eliminates the need for bot token creation and workspace admin approval by using user-level Slack authentication, reducing operational friction for teams that want AI-powered Slack integration without formal bot management processes

vs alternatives

Simpler deployment than Slack bot frameworks (Bolt, Hubot) because it requires no bot installation or admin approval, making it faster to prototype AI agents that read Slack context

slack channel and user metadata exposure via mcp resources

Medium confidence

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.

Solves for

I want my AI agent to know which channels exist and their descriptions before searching messagesI need to resolve user mentions and IDs to actual user profiles for context enrichmentI 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

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

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

What makes it unique

Exposes Slack workspace metadata as MCP resources rather than requiring agents to make raw API calls, allowing the MCP server to handle caching, pagination, and schema normalization transparently

vs alternatives

More efficient than agents making direct Slack API calls because metadata is cached and normalized into a consistent schema, reducing latency and API quota consumption

slack message posting and thread reply via mcp tools

Medium confidence

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.

Solves for

I want my AI agent to post summaries or alerts to a Slack channel as part of its workflowI need an agent to reply in a thread with analysis or answers without human interventionI 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

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)

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

What makes it unique

Implements message posting as MCP tools rather than resources, allowing agents to treat Slack posting as an action within their reasoning loop with proper error handling and validation

vs alternatives

Simpler than building a custom Slack bot because the MCP server handles authentication and API details, allowing any MCP-compatible agent to post to Slack without Slack-specific code

dual-transport mcp server with stdio and sse support

Medium confidence

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.

Solves for

I want to run the Slack MCP server locally as a subprocess in Claude Desktop or another MCP clientI need to deploy the Slack MCP server as a remote HTTP service accessible from multiple clientsI 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

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)

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

What makes it unique

Implements both Stdio and SSE transports in a single codebase, allowing the same MCP server to be deployed locally or remotely without transport-specific code paths or separate builds

vs alternatives

More flexible than single-transport MCP servers because it supports both local subprocess integration and remote HTTP deployment, reducing the need to maintain separate server implementations

proxy configuration and network resilience for slack api calls

Medium confidence

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.

Solves for

I need to deploy the Slack MCP server behind a corporate proxy that intercepts outbound trafficI want the server to automatically retry failed Slack API calls instead of failing immediatelyI 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

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

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

What makes it unique

Integrates proxy support and retry logic directly into the MCP server rather than requiring external middleware, simplifying deployment in restricted network environments

vs alternatives

Easier to deploy in corporate networks than generic MCP servers because proxy configuration is built-in and doesn't require separate reverse proxy or network layer configuration

no-permission slack integration without bot installation

Medium confidence

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.

Solves for

I want to integrate Slack with an AI agent without asking my workspace admin to approve a botI need a quick way to prototype Slack-powered AI features without formal bot setupI 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

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.)

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)

What makes it unique

Eliminates bot token management entirely by relying on user-level authentication, reducing the operational surface area and approval processes required for Slack integration

vs alternatives

Faster to deploy than bot-based Slack integrations because it skips bot creation, token management, and admin approval workflows, making it ideal for rapid prototyping

mcp resource schema exposure for agent introspection

Medium confidence

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.

Solves for

I want my AI agent to discover what Slack data is available without reading documentationI need the agent to understand the structure of Slack resources (channels, users, messages) automaticallyI 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

Requires

MCP client that supports resource schema introspection

LLM or agent capable of parsing and reasoning about JSON Schema

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

What makes it unique

Provides comprehensive JSON Schema definitions for all Slack resources, enabling agents to understand data structure and constraints through standard schema introspection rather than hardcoded knowledge

vs alternatives

More discoverable than raw API documentation because schemas are machine-readable and can be used by agents for planning and validation without human interpretation

thread-aware message context retrieval

Medium confidence

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.

Solves for

I want my AI agent to understand the full context of a Slack thread, not just individual messagesI need to retrieve a conversation thread and all its replies for analysis or summarizationI 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

Requires

Slack user token with chat:read scope

Thread timestamp (ts parameter) to identify the thread

Access to the channel containing the thread

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

What makes it unique

Reconstructs complete thread trees from Slack API responses, exposing thread structure as nested objects rather than flat message lists, making it easier for agents to reason about conversation flow

vs alternatives

More useful for agents than raw message search because it preserves conversation structure and context, enabling reasoning about discussion threads rather than isolated messages

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 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 user and workspace metadata lookupslack thread and reply managementslack message sending via mcp protocol

4 shared capabilities

MCP Server46

Slack MCP Server

Read and send Slack messages and manage channels via MCP.

slack workspace channel enumeration and metadata retrievalslack message posting with channel targetingslack message history retrieval with thread contextslack thread reply composition and posting

4 shared capabilities

MCP Server31

@sigmacomputing/slack-mcp-server

MCP server for interacting with Slack

slack channel and user lookup with context retrievalslack thread management and repliesslack message sending via mcp protocolmcp protocol translation and tool schema generation

4 shared capabilities

MCP Server29

slack-relay-mcp

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

mcp resource exposure for slack channels and messagesslack message relay via mcp protocolmulti-workspace slack support with workspace routingmcp tool schema for slack operations with function calling

4 shared capabilities

MCP Server23

Slack

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

slack channel listing and metadata retrievalmcp-compliant slack channel message postingmcp transport abstraction for slack api authentication

3 shared capabilities

Product17

Klavis AI

** - Open Source MCP Infra. Hosted MCP servers and MCP clients on Slack and Discord.

slack-native mcp client integration

1 shared capability

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
✓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
✓AI agents that need to communicate results or alerts back to Slack users
✓Automation workflows that use Slack as an output channel for notifications

Known 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
⚠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

Requirements

Slack workspace with user account that has access to channels being queriedSlack user token or OAuth token with chat:read and search:read scopesMCP 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 scopesMCP client capable of introspecting resource schemasWorkspace with at least basic channel and user setupSlack user token with chat:write scope

Input / Output

Accepts: query string (for message search), channel identifier (channel name or ID), timestamp range (for filtering by date), user identifier (for filtering by author), channel ID or name (for channel lookup), user ID or email (for user profile lookup), user group ID (for group membership queries), 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), MCP protocol messages (JSON-RPC format), HTTP POST requests (for SSE bidirectional communication), proxy URL (http://proxy.example.com:8080), proxy credentials (username:password), Slack API requests (internally), Slack user token, MCP schema introspection requests, thread timestamp (ts), channel ID, optional: limit on number of replies to retrieve

Produces: structured JSON with message metadata (timestamp, user, text, thread info), message thread objects with nested replies, channel and user reference objects, 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, confirmation object with message timestamp and channel ID, error response with failure reason if posting fails, MCP protocol responses (JSON-RPC format), Server-Sent Events stream (for SSE transport), successful Slack API responses (after retries if needed), error responses with retry information, authenticated access to Slack resources the user can access, JSON Schema definitions for each resource type, resource list with descriptions and query parameters, thread object with parent message and nested replies array, message objects with user, timestamp, text, and thread metadata

UnfragileRank

Adoption51%(30% weight)

Quality25%(25% weight)

Ecosystem60%(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

8 capabilities

Visit slack-mcp-server→

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Capabilities8 decomposed

slack workspace message retrieval and search via mcp

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

Simpler deployment than Slack bot frameworks (Bolt, Hubot) because it requires no bot installation or admin approval, making it faster to prototype AI agents that read Slack context

slack channel and user metadata exposure via mcp resources

Medium confidence

Solves for

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

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

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

What makes it unique

Exposes Slack workspace metadata as MCP resources rather than requiring agents to make raw API calls, allowing the MCP server to handle caching, pagination, and schema normalization transparently

vs alternatives

More efficient than agents making direct Slack API calls because metadata is cached and normalized into a consistent schema, reducing latency and API quota consumption

slack message posting and thread reply via mcp tools

Medium confidence

Solves for

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

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)

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

What makes it unique

Implements message posting as MCP tools rather than resources, allowing agents to treat Slack posting as an action within their reasoning loop with proper error handling and validation

vs alternatives

Simpler than building a custom Slack bot because the MCP server handles authentication and API details, allowing any MCP-compatible agent to post to Slack without Slack-specific code

dual-transport mcp server with stdio and sse support

Medium confidence

Solves for

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

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)

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

What makes it unique

Implements both Stdio and SSE transports in a single codebase, allowing the same MCP server to be deployed locally or remotely without transport-specific code paths or separate builds

vs alternatives

More flexible than single-transport MCP servers because it supports both local subprocess integration and remote HTTP deployment, reducing the need to maintain separate server implementations

proxy configuration and network resilience for slack api calls

Medium confidence

Solves for

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

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

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

What makes it unique

Integrates proxy support and retry logic directly into the MCP server rather than requiring external middleware, simplifying deployment in restricted network environments

vs alternatives

Easier to deploy in corporate networks than generic MCP servers because proxy configuration is built-in and doesn't require separate reverse proxy or network layer configuration

no-permission slack integration without bot installation

Medium confidence

Solves for

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

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.)

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)

What makes it unique

Eliminates bot token management entirely by relying on user-level authentication, reducing the operational surface area and approval processes required for Slack integration

vs alternatives

Faster to deploy than bot-based Slack integrations because it skips bot creation, token management, and admin approval workflows, making it ideal for rapid prototyping

mcp resource schema exposure for agent introspection

Medium confidence

Solves for

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

Requires

MCP client that supports resource schema introspection

LLM or agent capable of parsing and reasoning about JSON Schema

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

What makes it unique

vs alternatives

More discoverable than raw API documentation because schemas are machine-readable and can be used by agents for planning and validation without human interpretation

thread-aware message context retrieval

Medium confidence

Solves for

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

Requires

Slack user token with chat:read scope

Thread timestamp (ts parameter) to identify the thread

Access to the channel containing the thread

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

What makes it unique

Reconstructs complete thread trees from Slack API responses, exposing thread structure as nested objects rather than flat message lists, making it easier for agents to reason about conversation flow

vs alternatives

More useful for agents than raw message search because it preserves conversation structure and context, enabling reasoning about discussion threads rather than isolated messages

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

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slack-mcp-server

Capabilities8 decomposed

slack workspace message retrieval and search via mcp

slack channel and user metadata exposure via mcp resources

slack message posting and thread reply via mcp tools

dual-transport mcp server with stdio and sse support

proxy configuration and network resilience for slack api calls

no-permission slack integration without bot installation

mcp resource schema exposure for agent introspection

thread-aware message context retrieval

Related Artifactssharing capabilities

@sigmacomputing/slack-mcp-server

Slack MCP Server

@sigmacomputing/slack-mcp-server

slack-relay-mcp

Slack

Klavis AI

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

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slack-mcp-server

Capabilities8 decomposed

slack workspace message retrieval and search via mcp

slack channel and user metadata exposure via mcp resources

slack message posting and thread reply via mcp tools

dual-transport mcp server with stdio and sse support

proxy configuration and network resilience for slack api calls

no-permission slack integration without bot installation

mcp resource schema exposure for agent introspection

thread-aware message context retrieval

Related Artifactssharing capabilities

@sigmacomputing/slack-mcp-server

Slack MCP Server

@sigmacomputing/slack-mcp-server

slack-relay-mcp

Slack

Klavis AI

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to slack-mcp-server

Are you the builder of slack-mcp-server?

Get the weekly brief

Data Sources