Confluence MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Confluence through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
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({
"confluence": {
"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 Confluence, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Confluence MCP Server
Connect your Atlassian Confluence workspace to your AI assistant. Easily query your organization's knowledge base, search through technical documentation, and seamlessly generate new formatted pages right from the conversational interface.
LangChain's ecosystem of 500+ components combines seamlessly with Confluence through native MCP adapters. Connect 8 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
- Search Wiki Spaces — Quickly retrieve specific software architecture decisions, HR policies, or meeting notes without switching tabs.
- Read Pages — Extract the complete text and markdown-structured content of existing wiki pages for quick summaries.
- Create & Publish — Draft robust product requirements documents (PRDs) or meeting minutes using the AI, and publish them directly to your designated Confluence spaces.
The Confluence MCP Server exposes 8 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 Confluence to LangChain via MCP
Follow these steps to integrate the Confluence 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 8 tools from Confluence via MCP
Why Use LangChain with the Confluence MCP Server
LangChain provides unique advantages when paired with Confluence through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Confluence 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 Confluence queries for multi-turn workflows
Confluence + LangChain Use Cases
Practical scenarios where LangChain combined with the Confluence MCP Server delivers measurable value.
RAG with live data: combine Confluence tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Confluence, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Confluence tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Confluence tool call, measure latency, and optimize your agent's performance
Confluence MCP Tools for LangChain (8)
These 8 tools become available when you connect Confluence to LangChain via MCP:
add_page_comment
The body should be in storage format (HTML). Add a new comment to a Confluence page
create_page
Requires the space key, a title, and body content in Confluence storage format (HTML). The page is created at the root of the space. Create a new page in a Confluence space
get_page
Returns content body, space, version history, and metadata. Retrieve detailed information about a specific page
get_space_details
Returns metadata, description, homepage, and permissions overview. Retrieve detailed information about a specific space
list_page_comments
Returns inline and footer comments with author and content. Retrieve all comments for a specific Confluence page
list_pages
Optionally filter by space key. Supports pagination via start offset and limit. Retrieve a list of pages from Confluence
list_spaces
Retrieve a list of all spaces in Confluence
search_confluence
g. text ~ "project" AND type = "page"). Returns matching pages, blog posts, and comments. Search for content using Confluence Query Language (CQL)
Example Prompts for Confluence in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Confluence immediately.
"Search Confluence for documentation regarding our 'Q3 Migration Plan'."
"Create a new page in the 'Product' space summarizing our meeting notes from today."
"List all wiki pages currently under the space key 'HR'."
Troubleshooting Confluence MCP Server with LangChain
Common issues when connecting Confluence to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersConfluence + LangChain FAQ
Common questions about integrating Confluence MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
Connect Confluence with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Confluence to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.