
Jira Mcp
io.github.xcollantesPublished by GitHub account xcollantesv0.2.2Updated Oct 24, 2026
Feature-rich MCP for controlling Jira through LLM clients. Safe for corporate environments.
Overview
Lets an AI assistant read and manage Jira issues in natural language through a locally running MCP server.
- What you can do
- A local MCP server that connects an LLM client to Jira by wrapping the jira-cli command-line tool. It is described as offering full Jira control, including creating, updating, searching, transitioning, and managing tickets through natural language. The server runs on the user's machine and communicates over stdio with clients such as Cursor, Claude Desktop, Windsurf, or ChatGPT.
- When to use it
- Use it when you want an assistant to work with Jira tickets directly from your editor or desktop chat client, for example to look up issues, update fields, or move tickets through workflow transitions. It suits teams that want Jira actions driven by natural language without leaving their existing LLM client.
- Requirements
- Runs as a local process on desktop only. Requires the jira-cli tool to be installed and initialized with your Jira URL and credentials, plus the environment variables JIRA_API_TOKEN and JIRA_AUTH_TYPE (bearer, basic, or password). Installation options include prebuilt binaries, uv, or pipx. Network access to your Jira instance is needed.
Installation
In SourceWeft
- Open Jira Mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
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
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 tokenJIRA_AUTH_TYPE- Authentication type (bearerfor token,basicfor Jira account API token,passwordfor 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
This should initialize the Jira CLI by asking for your Jira URL and credentials.
Test Jira CLI
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.
Linux
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.
macOS
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:
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.
Windows
- Download
jira-mcp-windows.exefrom the Releases page. - Move the executable to a convenient location (e.g.,
C:\Program Files\jira-mcp\).
Add to your LLM client configuration:
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:
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:
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
How it works
- You enter some questions or prompt to a LLM Client such as the Claude Desktop, Cursor, Windsurf, or ChatGPT.
- The client sends your question to the LLM model (Sonnet, Grok, ChatGPT)
- 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.
- 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.
- The results are sent back to LLM.
- 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:
Once installed, ruff and pytest will automatically run when you commit. To run checks manually on 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:
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.
Source: README.md at commit 6886ab6
Tools
0Version history
1- v0.2.2LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts