How do I find my Slack Bot Token?

Log in to the [**Slack App Console**](https://api.slack.com/apps), select your App, go to **OAuth & Permissions**, and copy the 'Bot User OAuth Token'. Paste it below.

Can the agent check if a user is active?

Yes. Use the `get_presence` tool providing the User ID. Your agent will return the real-time presence status (active or away) for that specific user.

Is it possible to retrieve message history via the agent?

Yes. The `get_history` tool allows your agent to fetch recent messages from any channel, helping you audit conversations or monitor specific updates.

Slack Bot MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Slack Bot
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Slack Bot through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "slack-bot": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Slack Bot, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

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High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Slack Bot MCP Server

Empower your AI agent to orchestrate your entire workspace communication on Slack, the leading platform for team collaboration. By connecting your Slack bot to your agent, you transform complex workspace management into a natural conversation. Your agent can instantly list your channels, audit message history, and send updates without you ever touching a dashboard. Whether you are a community manager or a project lead, your agent acts as a real-time coordinator, ensuring your team is always informed and your communication data is organized.

LangChain's ecosystem of 500+ components combines seamlessly with Slack Bot through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Conversation Auditing — List all public channels in your workspace and retrieve detailed metadata, including purpose and topics.
Messaging Intelligence — Send and delete messages in any channel, and retrieve recent message history for real-time monitoring.
User Administration — Query workspace member lists, check user profiles, and monitor real-time presence (active/away).
Channel Governance — Autonomously join or leave public channels to keep your bot's scope relevant and efficient.
Operational Monitoring — Quickly retrieve detailed channel and user information to maintain strict organizational control.

The Slack Bot MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Slack Bot to LangChain via MCP

Follow these steps to integrate the Slack Bot MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Slack Bot via MCP

Why Use LangChain with the Slack Bot MCP Server

LangChain provides unique advantages when paired with Slack Bot through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Slack Bot MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Slack Bot queries for multi-turn workflows

Slack Bot + LangChain Use Cases

Practical scenarios where LangChain combined with the Slack Bot MCP Server delivers measurable value.

RAG with live data: combine Slack Bot tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Slack Bot, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Slack Bot tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Slack Bot tool call, measure latency, and optimize your agent's performance

Slack Bot MCP Tools for LangChain (10)

These 10 tools become available when you connect Slack Bot to LangChain via MCP:

delete_message

Delete a message from Slack

get_channel_info

Get details for a specific channel

get_history

Get message history for a channel

get_presence

Check if a user is active or away

get_user_info

Get details for a specific user

join_channel

Join a public channel

leave_channel

Leave a Slack channel

list_channels

List Slack channels

list_users

List all users in the workspace

send_message

Send a message to a Slack channel

Example Prompts for Slack Bot in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Slack Bot immediately.

"List all public channels in my Slack workspace."

"Send 'Good morning team!' to #general."

"Check if user ID U12345 is currently active."

Troubleshooting Slack Bot MCP Server with LangChain

Common issues when connecting Slack Bot to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Slack Bot + LangChain FAQ

Common questions about integrating Slack Bot MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.