How to integrate Jira MCP with Pydantic AI

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Introduction

This guide walks you through connecting Jira to Pydantic AI using the Composio tool router. By the end, you'll have a working Jira agent that can create a new bug in project alpha, assign issue jira-102 to sarah lee, add comment to ticket jira-207 with update, start a new sprint for the dev board through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Jira account through Composio's Jira 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 Jira
  • 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 Jira 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 Jira MCP server, and what's possible with it?

The Jira MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jira account. It provides structured and secure access to your Jira projects, so your agent can perform actions like creating issues, managing sprints, commenting on tasks, assigning work, and tracking releases on your behalf.

  • Automated issue creation and tracking: Let your agent create new bugs, tasks, or stories, and keep tabs on issues across your Jira projects.
  • Collaborative commenting and updates: Have your agent add rich-text comments or attachments to issues, keeping team communication seamless and up to date.
  • Effortless assignment and watcher management: Easily assign issues to teammates or add watchers, ensuring everyone stays in the loop and accountable.
  • Sprint and release planning: Empower your agent to create sprints, manage boards, and organize project milestones or versions for agile teams.
  • Issue linking and bulk operations: Direct your agent to link related issues or perform bulk creation of tasks, streamlining project workflows and dependencies.

Supported Tools & Triggers

Tools
Triggers
Add AttachmentUploads and attaches a file to a jira issue.
Add CommentAdds a comment using atlassian document format (adf) for rich text to an existing jira issue.
Add Watcher to IssueAdds a user to an issue's watcher list by account id.
Assign IssueAssigns a jira issue to a user, default assignee, or unassigns; supports email/name lookup.
Bulk Create IssuesCreates multiple jira issues (up to 50 per call) with full feature support including markdown, assignee resolution, and priority handling.
Create IssueCreates a new jira issue (e.
Link IssuesLinks two jira issues using a specified link type with optional comment.
Create ProjectCreates a new jira project with required lead, template, and type configuration.
Create SprintCreates a new sprint on a jira board with optional start/end dates and goal.
Create VersionCreates a new version for releases or milestones in a jira project.
Delete CommentDeletes a specific comment from a jira issue using its id and the issue's id/key; requires user permission to delete comments on the issue.
Delete IssueDeletes a jira issue by its id or key.
Delete VersionDeletes a jira version and optionally reassigns its issues.
Delete WorklogDeletes a worklog from a jira issue with estimate adjustment options.
Edit IssueUpdates an existing jira issue with field values and operations.
Find UsersSearches for jira users by email, display name, or username to find account ids; essential for assigning issues, adding watchers, and other user-related operations.
Get All Issue Type SchemesRetrieves all jira issue type schemes with optional filtering and pagination.
Get all projectsRetrieves all visible projects using the modern paginated jira api with server-side filtering and pagination support.
Get Issue StatusesRetrieves all available issue statuses from jira with details.
Get All UsersRetrieves all users from the jira instance including active, inactive, and other user states with pagination support.
Get CommentRetrieves a specific comment by id from a jira issue with optional expansions.
Get Current UserRetrieves detailed information about the currently authenticated jira user.
Get IssueRetrieves a jira issue by id or key with customizable fields and expansions.
Get Issue Link TypesRetrieves all configured issue link types from jira.
Get Issue PropertyRetrieves a custom property from a jira issue by key.
Get Issue ResolutionsRetrieves all available issue resolution types from jira.
Get issue typesRetrieves all jira issue types available to the user using the modern api v3 endpoint; results vary based on 'administer jira' global or 'browse projects' project permissions.
Get Issue Type SchemeGets a jira issue type scheme by id with all associated issue types.
Get Issue WatchersRetrieves users watching a jira issue for update notifications.
Get Issue WorklogsRetrieves worklogs for a jira issue with user permission checks.
Get Project VersionsRetrieves all versions for a jira project with optional expansion.
Get Issue Remote LinksRetrieves links from a jira issue to external resources.
Get TransitionsRetrieves available workflow transitions for a jira issue.
Get Issue VotesFetches voting details for a jira issue; requires voting to be enabled in jira's general settings.
Get WorklogsRetrieves worklogs for a specified jira issue.
List BoardsRetrieves paginated jira boards with filtering and sorting options.
List Issue CommentsRetrieves paginated comments from a jira issue with optional ordering.
List SprintsRetrieves paginated sprints from a jira board with optional state filtering.
Move Issues to SprintMoves one or more jira issues to a specified active sprint.
Remove Watcher from IssueRemoves a user from an issue's watcher list by account id.
Search Issues Using JQL (GET)Searches for jira issues using jql with pagination and field selection.
Search Issues Using JQL (POST)Searches for jira issues using jql via post request for complex queries; ideal for lengthy jql queries that might exceed url character limits
Search issuesAdvanced jira issue search supporting structured filters and raw jql.
Send Notification for IssueSends a customized email notification for a jira issue.
Transition IssueTransitions a jira issue to a different workflow state, with support for transition name lookup and user assignment by email.
Update CommentUpdates text content or visibility of an existing jira comment.

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 Jira
  • 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 Jira
  • MCPServerStreamableHTTP connects to the Jira 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 Jira
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["jira"],
    )
    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 Jira 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
jira_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[jira_mcp],
    instructions=(
        "You are a Jira assistant. Use Jira tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Jira endpoint
  • The agent uses GPT-5 to interpret user commands and perform Jira 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 Jira.\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
  • Jira 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 Jira 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 Jira
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["jira"],
    )
    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
    jira_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[jira_mcp],
        instructions=(
            "You are a Jira assistant. Use Jira 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 Jira.\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 Jira through Composio's Tool Router. With this setup, your agent can perform real Jira 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 + Jira 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 Jira MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Jira MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Jira MCP with Claude Agent SDK

Google ADK

Google ADK

Use Jira MCP with Google ADK

LangChain

LangChain

Use Jira MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Jira MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Jira MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Jira MCP with LlamaIndex

CrewAI

CrewAI

Use Jira MCP with CrewAI

FAQ

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

With a standalone Jira MCP server, the agents and LLMs can only access a fixed set of Jira tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Jira 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 Jira tools.

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

Yes, absolutely. You can configure which Jira 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 Jira data and credentials are handled as safely as possible.

Used by agents from

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