How to integrate Confluence MCP with Pydantic AI

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Introduction

This guide walks you through connecting Confluence to Pydantic AI using the Composio tool router. By the end, you'll have a working Confluence agent that can create a project documentation page in marketing space, add 'urgent' label to q3 planning page, publish team meeting summary as a blog post, create a private space for product roadmap drafts through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Confluence account through Composio's Confluence MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Confluence
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Confluence workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Confluence MCP server, and what's possible with it?

The Confluence MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Confluence account. It provides structured and secure access to your Confluence spaces, pages, and content, so your agent can perform actions like creating pages, publishing blog posts, organizing spaces, and managing metadata on your behalf.

  • Automated page and space creation: Instantly create new Confluence pages or entire spaces, empowering your agent to generate project documentation, wikis, or knowledge bases as needed.
  • Effortless blog post publishing: Let your agent draft and publish new blog posts within specified Confluence spaces to keep your team up-to-date and share knowledge seamlessly.
  • Content labeling and metadata management: Have your agent add labels and custom properties to pages, blog posts, or spaces, making it easy to organize, tag, and categorize information for better discoverability.
  • Private space setup and management: Direct your agent to create private, isolated workspaces for sensitive projects or teams, ensuring only authorized collaborators have access.
  • Custom content property automation: Empower your agent to attach or update custom metadata on pages, blog posts, spaces, or whiteboards, streamlining your internal documentation workflows.

Supported Tools & Triggers

Tools
Add Content LabelTool to add labels to a piece of content.
Get Space by IDTool to retrieve a confluence space by its id.
Create BlogpostTool to create a new confluence blog post.
Create Blogpost PropertyTool to create a property on a specified blog post.
Create Whiteboard PropertyTool to create a new content property on a whiteboard.
Create PageTool to create a new confluence page in a specified space.
Create Page PropertyTool to create a property on a confluence page.
Create Private SpaceTool to create a private confluence space.
Create SpaceTool to create a new confluence space.
Create Space PropertyTool to create a new property on a confluence space.
Create WhiteboardTool to create a new confluence whiteboard.
Delete Blogpost PropertyTool to delete a blog post property.
Delete Page Content PropertyTool to delete a content property from a page by property id.
Delete Whiteboard Content PropertyTool to delete a content property from a whiteboard by property id.
Delete PageTool to delete a confluence page.
Delete SpaceTool to delete a confluence space by its key.
Delete Space PropertyTool to delete a space property.
Get Attachment LabelsTool to list labels on an attachment.
Get AttachmentsTool to retrieve attachments of a confluence page.
Get Audit LogsTool to retrieve confluence audit records.
Get Blogpost by IDTool to retrieve a specific confluence blog post by its id.
Get Blogpost LabelsTool to retrieve labels of a specific confluence blog post by id.
Get Blogpost Like CountTool to get like count for a confluence blog post.
Get Blogpost OperationsTool to retrieve permitted operations for a confluence blog post.
Get BlogpostsTool to retrieve a list of blog posts.
Get Blog PostsTool to retrieve a list of blog posts.
Get Blog Posts For LabelTool to list all blog posts under a specific label.
Get Blogpost Version DetailsTool to retrieve details for a specific version of a blog post.
Get Blogpost VersionsTool to retrieve all versions of a specific blog post.
Get Child PagesTool to list all direct child pages of a given confluence page.
Get Blog Post Content PropertiesTool to retrieve all content properties on a blog post.
Get Page Content PropertiesTool to retrieve all content properties on a page.
Get Content RestrictionsTool to retrieve restrictions on a confluence content item.
Get Current UserTool to get information about the currently authenticated user.
Get Inline Comments for Blog PostTool to retrieve inline comments for a confluence blog post.
Get LabelsTool to retrieve all labels in a confluence site.
Get Page LabelsTool to retrieve labels of a specific confluence page by id.
Get Labels for SpaceTool to list labels on a space.
Get Labels for Space ContentTool to list labels on all content in a space.
Get Page AncestorsTool to retrieve all ancestors for a given confluence page by its id.
Get Page by IDTool to retrieve a confluence page by its id.
Get Page Like CountTool to get like count for a confluence page.
Get PagesTool to retrieve a list of pages.
Get Page VersionsTool to retrieve all versions of a specific confluence page.
Get Space by IDTool to retrieve a confluence space by its id.
Get Space ContentsTool to retrieve content in a confluence space.
Get Space PropertiesTool to get properties of a confluence space.
Get SpacesTool to retrieve a list of confluence spaces.
Get Anonymous UserTool to retrieve information about the anonymous user.
Search ContentSearches for content by filtering pages from the confluence v2 api with intelligent ranking.
Search UsersSearches for users using user-specific queries from the confluence query language (cql).
Update BlogpostTool to update a confluence blog post's title or content.
Update Blogpost PropertyTool to update a property of a specified blog post.
Update Page Content PropertyTool to update a content property on a confluence page.
Update Whiteboard Content PropertyTool to update a content property on a whiteboard.
Update PageTool to update an existing confluence page.
Update Space PropertyTool to update a space property.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Confluence
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Confluence
  • MCPServerStreamableHTTP connects to the Confluence MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Confluence
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["confluence"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Confluence tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
confluence_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[confluence_mcp],
    instructions=(
        "You are a Confluence assistant. Use Confluence tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Confluence endpoint
  • The agent uses GPT-5 to interpret user commands and perform Confluence operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Confluence.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Confluence API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Confluence and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Confluence
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["confluence"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    confluence_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[confluence_mcp],
        instructions=(
            "You are a Confluence assistant. Use Confluence tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Confluence.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Confluence through Composio's Tool Router. With this setup, your agent can perform real Confluence actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Confluence for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Confluence MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Confluence MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Confluence MCP with Claude Agent SDK

Google ADK

Google ADK

Use Confluence MCP with Google ADK

LangChain

LangChain

Use Confluence MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Confluence MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Confluence MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Confluence MCP with LlamaIndex

CrewAI

CrewAI

Use Confluence MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Confluence MCP?

With a standalone Confluence MCP server, the agents and LLMs can only access a fixed set of Confluence tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Confluence and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Pydantic AI?

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Confluence tools.

Can I manage the permissions and scopes for Confluence while using Tool Router?

Yes, absolutely. You can configure which Confluence scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Confluence data and credentials are handled as safely as possible.

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