Jira

This connector is optimized for AI agents. For the data replication connector, see Jira.

The Jira agent connector is a Python package that equips AI agents to interact with Jira through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

Connector for Jira API

Example prompts

The Jira connector is optimized to handle prompts like these.

Show me all open issues in my Jira instance
List recent issues created in the last 7 days
List all projects in my Jira instance
Show me details for the most recently updated issue
List all users in my Jira instance
Show me comments on the most recent issue
Show me worklogs from the last 7 days
Assign a recent issue to a teammate
Unassign a recent issue
Create a new task called 'Sample task' in a project
Create a bug with high priority
Update the summary of a recent issue to 'Updated summary'
Change the priority of a recent issue to high
Add a comment to a recent issue saying 'Please investigate'
Update my most recent comment
Delete a test issue
Remove my most recent comment
Transition {issue_key} to In Progress
Move {issue_key} to Done
What transitions are available for {issue_key}?
Log 2 hours of work on {issue_key}
Log 30 minutes on {issue_key} with a comment about what I did
Link {issue_key_1} as blocking {issue_key_2}
Create a 'relates to' link between {issue_key_1} and {issue_key_2}
What issues are assigned to {team_member} this week?
Find all high priority bugs in our current sprint
Show me overdue issues across all projects
What projects have the most issues?
Search for users named {user_name}

Unsupported prompts

The Jira connector isn't currently able to handle prompts like these.

Attach a file to {issue_key}
Add a watcher to {issue_key}

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Issues	API Search, Create, Get, Update, Delete, Context Store Search
Projects	API Search, Get, Context Store Search
Users	Get, List, API Search, Context Store Search
Issue Fields	List, API Search, Context Store Search
Issue Comments	List, Create, Get, Update, Delete, Context Store Search
Issue Worklogs	Get, List, Create, Context Store Search
Issues Assignee	Update
Issue Transitions	List, Create
Issue Links	Create

Jira API docs

See the official Jira API reference.

SDK installation

uv pip install airbyte-agent-sdk

SDK usage

Connectors can run in hosted or open source mode.

Hosted

In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

The connect() factory returns a fully typed JiraConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.jira import JiraConnector

connector = connect("jira", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.jira import JiraConnector

connector = connect("jira", workspace_name="<your_workspace_name>")

@tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.jira import JiraConnector

connector = connect("jira", workspace_name="<your_workspace_name>")

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@JiraConnector.tool_utils(framework="openai_agents")
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Jira Assistant", tools=[jira_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.jira import JiraConnector

connector = connect("jira", workspace_name="<your_workspace_name>")

mcp = FastMCP("Jira Agent")

@mcp.tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = JiraConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = JiraConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

@tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = JiraConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@JiraConnector.tool_utils(framework="openai_agents")
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Jira Assistant", tools=[jira_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = JiraConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

mcp = FastMCP("Jira Agent")

@mcp.tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Open source

In open source mode, you provide API credentials directly to the connector.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.connectors.jira.models import JiraAuthConfig

connector = JiraConnector(
    auth_config=JiraAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.connectors.jira.models import JiraAuthConfig

connector = JiraConnector(
    auth_config=JiraAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

@tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.connectors.jira.models import JiraAuthConfig

connector = JiraConnector(
    auth_config=JiraAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@JiraConnector.tool_utils(framework="openai_agents")
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Jira Assistant", tools=[jira_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.connectors.jira.models import JiraAuthConfig

connector = JiraConnector(
    auth_config=JiraAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

mcp = FastMCP("Jira Agent")

@mcp.tool
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    """Execute Jira connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Authentication

For all authentication options, see the connector's authentication documentation.

Version information

Connector version: 1.1.9

Example prompts​

Unsupported prompts​

Entities and actions​

Jira API docs​

SDK installation​

SDK usage​

Hosted​

Open source​

Authentication​

Version information​