Jira MCP

Jira MCP for controlling Jira through Jira Command Line.

Installation

Install jira-cli

The MCP server uses the jira-cli to execute Jira commands.

Follow the installation instructions for your operating system: https://github.com/ankitpokhrel/jira-cli?tab=readme-ov-file#installation

Get Jira API Key

Depending on your implementation of Jira (Cloud or Self-Hosted), you will need to use a different authentication type.

Add these to your .bashrc or .zshrc file, or other shell configuration file.

# https://id.atlassian.com/manage-profile/security/api-tokens
export JIRA_API_KEY=""

# `bearer` for token,
# `basic` for Jira account API token
# `password` for Jira account password
export JIRA_AUTH_TYPE="basic"

Make sure to source the file after adding the credentials.

source ~/.bashrc

Other ways to add credentials to your environment: https://github.com/ankitpokhrel/jira-cli/discussions/356

Start Jira CLI

jira init

This should initialize the Jira CLI by asking for your Jira URL and credentials.

Test Jira CLI

jira issue list

This should return a list of issues in Jira.

MCP Server: Option 1: Development setup with uv

Get repo:

git clone https://github.com/xcollantes/jira-mcp.git
cd jira-mcp

Add MCP server to your choice of LLM client:

NOTE: You will need to look up for your specific client on how to add MCPs.

Usually the JSON file for the LLM client will look like this:

{
  "mcpServers": {
    "jira": {
      "command": "uv",
      "args": ["--directory", "/ABSOLUTE/PATH/TO/REPO/ROOT", "run", "python", "-m", "src.main"]
    }
  }
}

This will tell your LLM client application that there's a tool that can be called by calling uv --directory /ABSOLUTE/PATH/TO/REPO run python -m src.main.

Install UV: https://docs.astral.sh/uv/getting-started/installation/

MCP Server: Option 2: Install globally with pipx

# Install pipx if you haven't already
brew install pipx
pipx ensurepath

# Clone and install the MCP server
git clone https://github.com/xcollantes/jira-mcp.git
cd jira-mcp
pipx install -e .

How it works

1. You enter some questions or prompt to a LLM Client such as the Claude Desktop, Cursor, Windsurf, or ChatGPT.
2. The client sends your question to the LLM model (Sonnet, Grok, ChatGPT)
3. LLM analyzes the available tools and decides which one(s) to use
   • The LLM you're using will have a context of the tools and what each tool is meant for in human language.
   • Alternatively without MCPs, you could include in the prompt the endpoints and a description on each endpoint for the LLM to "call on". Then you could copy and paste the text commands into the terminal on your machine.
   • MCPs provide a more deterministic and standardized method on LLM-to-server interactions.
4. The client executes the chosen tool(s) through the MCP server.
   • The MCP server is either running local on your machine or an endpoint hosting the MCP server remotely.
5. The results are sent back to LLM.
6. LLM formulates a natural language response and one or both of the following happen:
   • The response is displayed to you with data from the MCP server
   • Some action is performed using the MCP server

Development

Logging

Do not use print statements for logging. Use the logging module instead. Writing to stdout will corrupt the JSON-RPC messages and break your server.

Docstrings / Tool decorator parameters

MCP.tools decorator parameters are especially important as this is the human readable text that the LLM has context of. This will be treated as part of the prompt when fed to the LLM and this will decide when to use each tool.

Architecture

MCP follows a client-server architecture where an MCP host (an AI application like Cursor or ChatGPT desktop) establishes connections to one or more MCP servers. The MCP host accomplishes this by creating one MCP client for each MCP server. Each MCP client maintains a dedicated connection with its corresponding MCP server.

https://modelcontextprotocol.io/docs/learn/architecture

Pitfalls / Troubleshooting

Edit the jira-cli config file

On MacOS:

/Users/<your-username>/.config/.jira/.config.yml

404 error when using jira init

If you get a 404 error when using jira init, you may need to edit the jira-cli config file to point to the correct Jira instance. There are only 3 possible values for the auth type so try each one. basic, password, or bearer.