taskmaster-mcp
MCP server providing Jira tools to fetch, create, update tickets, manage attachments, and generate AI-powered ticket content.
README
taskmaster-mcp
A TypeScript MCP server that gives Claude, Cursor and other MCP hosts a set of Jira tools. Built on FastMCP + Express. This is a trimmed-down version of something we actually run internally.
Treat this as your first week on the team: get it running, poke around, find something worth improving, and ship it as a pull request.
We haven't told you what to build. Deciding that is part of the exercise.
1. Make your own copy
Use the template — don't fork.
- On the repo page, click Use this template → Create a new repository.
- Name it whatever you like.
- Set the visibility to Private.
- Create the repository.
Then add @laurence-barry and @dave-r-cintra as collaborators: your repo → Settings → Collaborators → Add people.
2. Get a Jira instance
You'll need your own Jira Cloud site — the free tier takes about five minutes and doesn't need a card.
- Sign up at atlassian.com/software/jira/free.
- Create a project. A Scrum template gives you a backlog and sprint board,
which
next_jira_taskexpects. Note the project key (e.g.PROJ). - Generate an API token at id.atlassian.com/manage-profile/security/api-tokens.
Spend a couple of minutes creating issues with a mix of types, statuses and priorities, plus a subtask and an epic link. Several tools walk those relationships and an empty board won't show you much.
3. Run it
Requires Node 22+.
npm install
cp example.env .env # fill in your Jira values
npm run dev # http://localhost:3000
JIRA_API_URL=https://your-domain.atlassian.net
JIRA_EMAIL=the-email-you-signed-up-with@example.com
JIRA_API_TOKEN=<token from step 3 above>
JIRA_PROJECT=<your project key, e.g. PROJ>
ALLOW_UNAUTHENTICATED_LOCAL=true
Four tools call Claude for ticket content — add ANTHROPIC_API_KEY from
console.anthropic.com if you want those. The
others work without it.
Connect an MCP client
Point the client at http://localhost:3000/mcp.
Claude Code:
claude mcp add --transport http taskmaster http://localhost:3000/mcp
Cursor — in .cursor/mcp.json:
{
"mcpServers": {
"taskmaster": {
"url": "http://localhost:3000/mcp"
}
}
}
With ALLOW_UNAUTHENTICATED_LOCAL=true, localhost requests use your .env
credentials. Try the tools out properly before you write any code — most of what's
interesting here only shows up once you've used the thing.
curl -s http://localhost:3000/health
4. Build something
Pick a feature or improvement you think the project should have, and implement it.
Good choices tend to be things you noticed while using it — a rough edge, a gap in the tool coverage, something that broke when you fed it real data. We're more interested in a small, well-judged, complete change than a large half-finished one.
Work in a branch and open a pull request against main in your own repo. Don't
merge it — leave it open for us to review.
Your PR description should cover
- What you built, briefly.
- Why you chose it over the other things you noticed.
- Trade-offs you made, and anything you'd do differently with more time.
This matters as much as the code. A short, clear write-up beats a long one.
What we're looking for
| Judgement | Did you pick something that genuinely improves the project, and can you say why? |
| Craft | Does the change fit the codebase's existing patterns and conventions? |
| Communication | Does the PR explain itself to someone who wasn't there? |
| Tool use | How well do you work with AI — steering it, checking it, rejecting it? |
We won't be scoring you on the size of the diff.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.