TAPD MCP. Manage Bugs, Stories, and Tasks via AI Command.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
TAPD MCP Server connects your AI client directly to the Tencent TAPD platform. Forget clicking through complex web interfaces just to track development progress.
Your agent handles everything: list all workspaces, create stories and bugs, manage tasks, or check sprint milestones—all using natural language commands.
What your AI agents can do
Create bug
Files a brand new defect report into TAPD for tracking.
Create story
Adds a new requirement or feature story to the development backlog.
Create task
Creates an actionable, granular task within a specific workspace.
List all development environments you have access to via list_workspaces.
File requirements as new stories in a specific workspace using create_story.
Generate a new defect ticket immediately with the create_bug tool.
Retrieve an organized list of bugs existing in a target workspace using list_bugs.
Create new daily tasks or retrieve lists of tasks across workspaces via create_task or list_tasks.
Browse all project iterations (sprints) to track milestones with the list_iterations tool.
Ask AI about this MCP
Supported MCP Clients
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TAPD MCP Server: 10 Tools for Agile Development
Use these tools to automate common development lifecycle actions in TAPD—from listing all projects to creating new defects.
019d8487create bug
Files a brand new defect report into TAPD for tracking.
019d8487create story
Adds a new requirement or feature story to the development backlog.
019d8487create task
Creates an actionable, granular task within a specific workspace.
019d8487get workspace
Fetches detailed metadata about a single TAPD workspace environment.
019d8487list bugs
Retrieves an overview of all open and closed bugs in a given workspace.
019d8487list iterations
Lists the planned sprints or project milestones for a workspace.
019d8487list members
Retrieves a list of all users and team members associated with a workspace.
019d8487list stories
Provides a structured list of feature requirements (stories) in a specified workspace.
019d8487list tasks
Retrieves all granular development tasks for a given project space.
019d8487list workspaces
Gets a list of every TAPD workspace you have access to.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with TAPD, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Forget clicking through that brutal TAPD web interface just to keep up with development. This server connects your AI client straight into the Tencent TAPD platform, letting your agent handle everything—from tracking requirements to logging critical bugs—using natural language commands. You tell it what you need done; your agent does the heavy lifting.
To start working, your agent first uses list_workspaces to get a list of every development environment you have access to. Once you know which project space you're in, you can run get_workspace, and your agent fetches all the detailed metadata about that specific workspace for context.
When it comes to requirements, your agent handles story management using list_stories to pull up a structured list of feature requirements within any given space. If someone pitches a brand new idea or requirement, you just ask your agent to run create_story, and it files the full requirement into the development backlog.
Tracking defects is straightforward. Your agent uses list_bugs to retrieve an organized overview of all open and closed bugs existing in your target workspace. If you find a defect, simply ask your agent to run create_bug, and it immediately files that new defect report for tracking.
Managing the actual work is where this setup shines. To create actionable tasks, you tell your agent to run create_task, which generates a granular task inside a specific workspace. You can also pull up all existing daily development items by asking your agent to use list_tasks across various project spaces.
For tracking progress against goals, your agent uses list_iterations to browse all planned sprints or major project milestones for any given workspace. If you need to see who's on the team, it runs list_members, pulling up a list of every user and team member associated with that space.
Basically, when you have a development question—whether it’s listing out all active workspaces via list_workspaces or filing a critical defect using create_bug—your agent executes the action instantly. You don't need to know which tab to click or where to find specific IDs. It keeps your entire development pipeline organized and your team aligned, whether you run Scrum or Kanban.
Your agent treats requirement tracking, bug management, task creation, and sprint planning like just another conversation.
How TAPD MCP Works
- 1 Subscribe to the server and provide your TAPD API User/Password credentials.
- 2 Ask your AI agent a natural language command, such as 'List all stories for Mobile App V2.'
- 3 The agent calls the appropriate tool (e.g.,
list_stories), gets the data payload, and formats it into a readable response.
The bottom line is that you talk to your development platform using natural language commands instead of clicking through dashboards.
Who Is TAPD MCP For?
This is for the Product Manager who hates spending hours manually auditing Jira/TAPD backlogs, or the QA Engineer who's tired of copy-pasting bug IDs into Slack. If your job involves keeping track of features, bugs, and tasks across multiple projects, you need this.
Audits backlogs, tracks feature requirements by calling list_stories, and monitors iteration progress to report status updates.
Reports defects using create_bug instantly, then checks all existing issues with list_bugs across multiple projects.
Manages assigned tasks by calling list_tasks, and updates status or flags new blockers directly from the agent interface.
What Changes When You Connect
- Stop switching tabs. Your agent manages the entire lifecycle—listing workspaces with
list_workspaces, creating stories withcreate_story, and filing defects withcreate_bug—all without leaving your chat window. - Get a single view of project progress. Use
list_storiesandlist_taskstogether to see every requirement and sub-task assigned, instantly mapping out the current sprint scope. - No more manual data gathering. If you need to know who's on the team or what milestones are coming up, call
list_membersorlist_iterations. The data comes straight to your chat. - Speed when tracking issues matters. Use
list_bugsto immediately check all defects across a project instead of navigating through filtering menus. - Stay in control of the development process. Your agent doesn't just read data; it writes it back using tools like
create_taskandcreate_story, keeping your backlog current.
Real-World Use Cases
The End-of-Day Status Report
A Product Manager needs to report status across three different projects. Instead of logging into TAPD three times, they ask their agent: 'List stories and bugs for Project Alpha, Beta, and Gamma.' The agent runs list_stories and list_bugs repeatedly, consolidating the data points into one clear summary.
The Urgent Bug Filing
A QA Engineer finds a crash on release. They don't open the bug tracker; they tell their agent: 'File a new high-priority bug in the Mobile App V2 workspace.' The agent runs create_bug, handles all required fields, and confirms it’s logged.
Project Kickoff Check
A team is starting a massive feature. To scope it, the lead asks their agent: 'What workspaces do we need to use?' The agent runs list_workspaces, giving an immediate list of all available containers for the new work.
Audit Team Assignments
An Agile Coach needs to know who is assigned tasks in a complex project. They ask: 'Who are the members and what tasks are they handling?' The agent runs list_members and then list_tasks, giving a full accountability map.
The Tradeoffs
Trying to find everything in one prompt
Asking the AI: 'Give me all the stories, bugs, tasks, and members for Project X.' This often fails because the agent needs specific tool calls in sequence.
→
Break it down. Start by finding the context with list_workspaces. Then run specific tools like list_stories or list_tasks one at a time to get accurate data payloads.
Manually copying IDs and status updates
The engineer manually copies 15 bug IDs from the TAPD web UI into Slack for review. This is slow, error-prone, and loses context.
→
Use list_bugs to pull all necessary data directly into your chat window. Your agent handles the formatting, saving you the copy/paste time.
Assuming a single 'overview' tool exists
Expecting a magical 'get_project_status()' function that returns everything instantly. The platform separates concerns for data integrity.
→
You must chain the tools: start with list_workspaces to select the target, then run list_stories, followed by list_tasks. This ensures you get accurate, separated datasets.
When It Fits, When It Doesn't
Use this server if your primary bottleneck is switching between development tools. If you spend more time navigating TAPD's web UI than actually doing product work, this saves hours.
Don't use it if you only need to read data and don't need the AI agent to perform actions (like creating a story or logging a bug). For simple reading, some basic API wrappers might suffice. But if your goal is process management—creating records, updating status, tracking progress across multiple views—this is what you need.
If you are building complex workflows that involve external systems (e.g., connecting TAPD data to Jira or GitHub), this server acts as a powerful source but won't handle the cross-platform orchestration itself. Use it for clean, structured access to TAPD data only.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TAPD. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Tracking project status means endless dashboard clicking.
Right now, checking a project's health requires navigating deep into the TAPD platform: check Stories in one tab, then switch to Tasks, and finally open the Bugs list. You end up with dozens of tabs, constantly switching focus just to stitch together 'What is done?'
With this MCP server, you ask your agent for the status. It runs `list_stories` and `list_tasks` in the background, pulls all relevant data, and presents a unified report directly back to your chat. You get the full picture without opening any other browser tabs.
TAPD MCP Server: Log defects with `create_bug`.
The old way means logging into the bug tracker, finding the right project space, clicking 'New Bug,' filling out a form, and remembering to assign the correct priority. This process takes three minutes of manual effort and context switching.
Now, you just tell your agent: 'Create a high-priority bug report for Project X.' The `create_bug` tool handles the project selection, data structure, and filing instantly. It's done.
Common Questions About TAPD MCP
How do I list all projects using the TAPD MCP Server? +
Run the list_workspaces tool. This retrieves a comprehensive list of every workspace you have access to, helping you pinpoint which project you need data from.
Can I create a story or bug without knowing the workspace ID using TAPD MCP Server? +
No, you must first know the context. You should use list_workspaces to find your target environment, and then specify that workspace when calling create_story or create_bug.
What is the difference between listing stories and listing tasks with TAPD MCP Server? +
Stories are high-level requirements (the 'what'). Tasks are granular, actionable steps assigned to people (the 'how'). Use list_stories for scope; use list_tasks for daily work.
If I need to check a project's progress, which tool should I use with TAPD MCP Server? +
You should combine tools. Start by calling list_iterations to see the planned timeline (the 'when'), and then use list_bugs or list_tasks to see if work is actually happening.
What information do I need to connect when using the `list_workspaces` tool? +
You must provide your specific TAPD API User and API Password. These credentials are necessary for all operations, including listing workspaces, so make sure they're configured correctly in Vinkius.
Using the `list_members` tool, what details can my AI agent retrieve about team members? +
The list_members tool returns key roster information for a given workspace. You get member names and their assigned roles, which helps your agent manage assignments or check who is available to work on a feature.
When I run `get_workspace`, what metadata does the MCP Server provide about the project? +
The get_workspace tool pulls comprehensive details for that workspace. You get core metadata like its unique ID and description, giving your agent full context before it attempts to create stories or bugs.
What is the functional difference between using `create_story` versus `create_task`? +
Stories track high-level feature requirements. Tasks handle the specific, granular steps needed to complete that work. Use create_story for defining what needs to happen; use create_task when you need actionable items checked off a list.
How do I find my TAPD API User and Password? +
Log in to TAPD, go to Company Management (公司管理) → Open Integration (开放集成) → API Account Management (API 账号管理), and generate your credentials there.
Can I report bugs directly through the agent? +
Yes. Use the create_bug tool. You will need to provide the workspace ID and a title. You can also add a detailed description with reproduction steps for your developers.
What is a 'Story' in TAPD? +
In TAPD, a Story represents a product requirement or feature. You can manage these using the list_stories and create_story tools to keep your product backlog organized.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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