How to Use the Confluence MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK pipeline search Confluence automatically?

Yes, you use the searchconfluence tool to run queries against your instance. The agent receives the results directly in its execution context.

Q: How do I add comments to Confluence pages from OpenAI Agents SDK?

You pass the HTML-formatted string to the addpagecomment tool. The server handles the API call to your Confluence instance.

Q: Does the Confluence MCP server support pagination in OpenAI Agents SDK?

Yes, the listpages tool accepts offset and limit parameters. This prevents memory issues when your agent scans large spaces.

Read, write, and organize your Confluence docs directly through your OpenAI Agents SDK production pipeline.

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Connect Confluence MCP to OpenAI Agents SDK

Create your Vinkius account to connect Confluence to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Confluence with OpenAI Agents SDK

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Manage documentation in OpenAI Agents SDK

Your agent handles page lifecycle tasks without leaving your Python code. Use `create_page` to push documentation updates to specific spaces as part of your deployment workflow. You can also use `add_page_comment` to attach feedback or status updates to existing pages, keeping your team in the loop.

Automated wiki retrieval for OpenAI Agents SDK

Stop digging through search bars and let your agent pull what you need. `get_page` and `list_pages` fetch raw content and version history so your agent has the right context. Use `search_confluence` to execute complex CQL queries. It returns the exact match for pages or blog posts immediately.

Full space awareness for OpenAI Agents SDK

Your agent understands the hierarchy of your workspace by calling `get_space_details`. It maps out permissions and metadata to ensure actions stay within defined boundaries. `list_spaces` gives your code a clear view of all available environments. This MCP server keeps your agent informed of where data lives.

Setup guide

Set up Confluence MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Confluence tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Confluence tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Confluence tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Confluence Agent",
            instructions="You have access to Confluence tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Confluence MCP in OpenAI Agents SDK

Yes, you use the `search_confluence` tool to run queries against your instance. The agent receives the results directly in its execution context.

You pass the HTML-formatted string to the `add_page_comment` tool. The server handles the API call to your Confluence instance.

Yes, the `list_pages` tool accepts offset and limit parameters. This prevents memory issues when your agent scans large spaces.

The server returns a standard error response. You can catch this in your agent's tracing logic to log the failure and retry.

All communication happens over an encrypted connection using your endpoint token. We never store your wiki content or page credentials on our servers.

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