Which Base URL should I use for my account?

If you are using the international version, use `https://api.coze.com`. For the Chinese version, use `https://api.coze.cn`.

Can I automatically list all published bots in a space?

Yes! Use the `list_bots` tool with your Space ID. Your agent will return a list of all bots that have been published and are ready for interaction.

How do I upload a new document to my knowledge base?

Use the `upload_document` tool with the target Dataset ID and the raw text content. Your agent will handle the ingestion process into the Coze RAG engine.

Coze MCP Server for LangChain 11 tools — connect in under 2 minutes

Name: Coze
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Coze through 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({
        "coze": {
            "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 Coze, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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 Coze MCP Server

Connect your AI agents to Coze (扣子), the advanced bot orchestration platform by ByteDance. This MCP provides 11 tools to manage the full lifecycle of your bots, from chat interactions to knowledge base document ingestion.

LangChain's ecosystem of 500+ components combines seamlessly with Coze through native MCP adapters. Connect 11 tools via 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

Bot Interaction — Chat with published bots and handle multi-turn conversations with persistent history
Knowledge Engineering — Upload, list, and delete documents in knowledge base datasets for RAG optimization
Workspace Management — List available spaces and published bots to monitor your AI ecosystem
Action Handling — Submit tool outputs when bots require human-in-the-loop or external plugin results

The Coze MCP Server exposes 11 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 Coze to LangChain via MCP

Follow these steps to integrate the Coze 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 11 tools from Coze via MCP

Why Use LangChain with the Coze MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Coze 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 Coze queries for multi-turn workflows

Coze + LangChain Use Cases

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

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

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

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

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

Coze MCP Tools for LangChain (11)

These 11 tools become available when you connect Coze to LangChain via MCP:

clear_conversation

Clear all messages from a conversation session

create_chat

Send a message to a Coze bot and get a response

delete_document

Delete documents from a dataset by ID

get_conversation_history

Retrieve the message list from a conversation

list_bots

List published bots in a specific Coze Space

list_datasets

List knowledge base datasets in a Coze Space

list_workspaces

List available Coze workspaces/spaces

publish_bot

Publish a Coze Bot draft

submit_tool_outputs

Submit outputs for tools/plugins required by the bot

upload_document

Upload a raw text document to a Knowledge Base

upload_file_url

Upload an external file URL to Coze storage

Example Prompts for Coze in LangChain

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

"Chat with bot 'bot_123' and ask 'Tell me about the history of Tokyo'."

"List all active workspaces in my Coze account."

"Upload the content of 'manual.txt' to dataset 'ds_999'."

Troubleshooting Coze MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Coze + LangChain FAQ

Common questions about integrating Coze 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.