Jira Mcp

io.github.xcollantes由 GitHub 帳號 xcollantes 發布v0.2.2更新於 Oct 24, 2026

Feature-rich MCP for controlling Jira through LLM clients. Safe for corporate environments.

已驗證STDIO僅桌面效率與工作流開發者工具

概覽

AI 產生的概覽

讓 AI 助理透過在本機執行的 MCP 伺服器,以自然語言讀取與管理 Jira 議題。

可以做什麼
這是一個本機 MCP 伺服器,透過包裝 jira-cli 命令列工具,把 LLM 用戶端連接到 Jira。依照說明,它提供完整的 Jira 控制能力,包括以自然語言建立、更新、搜尋、轉換狀態與管理議題。伺服器在使用者機器上執行,透過 stdio 與 Cursor、Claude Desktop、Windsurf 或 ChatGPT 等用戶端通訊。
適用情境
當你希望助理直接在編輯器或桌面聊天用戶端中處理 Jira 議題時使用,例如查詢問題、更新欄位,或推動議題完成工作流程轉換。它適合想在不離開現有 LLM 用戶端的情況下,以自然語言驅動 Jira 操作的團隊。
執行需求
僅以本機程序在桌面環境執行。需要安裝並初始化 jira-cli,設定 Jira 網址與認證資訊,並設定環境變數 JIRA_API_TOKEN 與 JIRA_AUTH_TYPE(bearer、basic 或 password)。安裝方式包括預先建置的執行檔、uv 或 pipx。需要能連線到你的 Jira 執行個體的網路。
安裝前請注意
伺服器需要密鑰 JIRA_API_TOKEN,該權杖可存取你的 Jira 帳號;請將它放在 MCP 用戶端的 env 設定中,而不是 shell 設定檔裡。它可以建立、更新、轉換狀態或以其他方式修改 Jira 議題,因此助理的操作可能改動真實專案資料。README 表示它在本機執行且不與第三方分享資料,但議題內容仍會傳送給你所選的 LLM 供應商。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Jira Mcp,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

[Jira MCP]

Jira MCP - The most feature-rich MCP server for Jira

[Claude Demo]

[Python] [MCP] [pytest] [ruff] [uv] [CI] [License] [Version]

The most powerful and feature-rich MCP server for Jira integration. Control Jira through AI-powered LLM clients like Cursor, Claude Desktop, Windsurf, and ChatGPT using the Model Context Protocol.

Why Jira MCP is the Best Choice

  • Enterprise Ready: Runs completely locally with no third-party data sharing. Safe for corporate environments.
  • Full-Featured: Complete Jira control including create, update, search, transition, and manage tickets using natural language.
  • Easy Setup: Pre-built binaries for Linux, macOS, and Windows. Get started in minutes.
  • Actively Maintained: Open source, MIT licensed, with regular updates and community support.
  • AI-Native: Built specifically for the Model Context Protocol to provide the best AI-to-Jira experience.

Getting started

Jira MCP Server

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 Token

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

Get your API token from: https://id.atlassian.com/manage-profile/security/api-tokens

You will need to set the following environment variables:

  • JIRA_API_TOKEN - Your Jira API token
  • JIRA_AUTH_TYPE - Authentication type (bearer for token, basic for Jira account API token, password for Jira account password)

Recommended: Pass these variables in your MCP client configuration using the env field (shown in the configuration examples below). This is more reliable than shell environment variables because GUI applications like Cursor and Windsurf do not inherit variables from .bashrc or .zshrc.

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

Start Jira CLI

bash
jira init

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

Test Jira CLI

bash
jira issue list

This should return a list of issues in Jira.

MCP Server: Option 1: Download binaries (Recommended)

Download the latest release for your operating system from the Releases page.

Operating SystemBinary
Linuxjira-mcp-linux
Windowsjira-mcp-windows.exe
macOS (Apple Silicon)jira-mcp-macos-apple-silicon-arm64
macOS (Intel)jira-mcp-macos-x64
Linux
bash
# Download the binarycurl -L -o jira-mcp https://github.com/xcollantes/jira-mcp/releases/latest/download/jira-mcp-linux
# Make it executablechmod +x jira-mcp
# Move to a directory in your PATH (optional)sudo mv jira-mcp /usr/local/bin/

Add to your LLM client configuration:

NOTE: Make sure to replace /usr/local/bin/jira-mcp with the path to the binary on your machine if you moved it to a different location.

json
{  "mcpServers": {    "jira": {      "command": "/usr/local/bin/jira-mcp",      "env": {        "JIRA_API_TOKEN": "your-api-token",        "JIRA_AUTH_TYPE": "basic"      }    }  }}
macOS
bash
# For Apple Silicon (M1/M2/M3)curl -L -o jira-mcp https://github.com/xcollantes/jira-mcp/releases/latest/download/jira-mcp-macos-apple-silicon-arm64
# For Intel Macscurl -L -o jira-mcp https://github.com/xcollantes/jira-mcp/releases/latest/download/jira-mcp-macos-x64
# Make it executablechmod +x jira-mcp
# Move to a directory in your PATH (optional)sudo mv jira-mcp /usr/local/bin/

Note: macOS may block the binary on first run. If you see a security warning, go to System Settings > Privacy & Security and click Allow Anyway, or run:

bash
xattr -d com.apple.quarantine /usr/local/bin/jira-mcp

Add to your LLM client configuration:

NOTE: Make sure to replace /usr/local/bin/jira-mcp with the path to the binary on your machine if you moved it to a different location.

json
{  "mcpServers": {    "jira": {      "command": "/usr/local/bin/jira-mcp",      "env": {        "JIRA_API_TOKEN": "your-api-token",        "JIRA_AUTH_TYPE": "basic"      }    }  }}
Windows
  1. Download jira-mcp-windows.exe from the Releases page.
  2. Move the executable to a convenient location (e.g., C:\Program Files\jira-mcp\).

Add to your LLM client configuration:

json
{  "mcpServers": {    "jira": {      "command": "C:\\Program Files\\jira-mcp\\jira-mcp-windows.exe",      "env": {        "JIRA_API_TOKEN": "your-api-token",        "JIRA_AUTH_TYPE": "basic"      }    }  }}

NOTE: Make sure to replace C:\\Program Files\\jira-mcp\\jira-mcp-windows.exe with the path to the binary on your machine if you moved it to a different location.

MCP Server: Option 2: Development setup with uv

Get repo:

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

json
{  "mcpServers": {    "jira": {      "command": "uv",      "args": [        "--directory",        "/ABSOLUTE/PATH/TO/REPO/ROOT",        "run",        "python",        "-m",        "src.main"      ],      "env": {        "JIRA_API_TOKEN": "your-api-token",        "JIRA_AUTH_TYPE": "basic"      }    }  }}

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 3: Install globally with pipx

bash
# Install pipx if you haven't alreadybrew install pipxpipx ensurepath
# Clone and install the MCP servergit clone https://github.com/xcollantes/jira-mcp.gitcd jira-mcppipx 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.

Pre-commit

This project uses pre-commit to run ruff linting and formatting checks, and pytest tests before each commit.

To set up pre-commit hooks:

bash
uv syncuv run pre-commit install

Once installed, ruff and pytest will automatically run when you commit. To run checks manually on all files:

bash
uv run pre-commit run --all-files

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:

text
/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.

來源:README.md,提交 6886ab6

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.2.2最新Sep 16, 2026

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