
ProductPlan MCP Server
io.github.olgasafonova由 GitHub 账号 olgasafonova 发布v6.0.0更新于 Oct 25, 2026
Talk to roadmaps with AI. Query and manage bars, OKRs, ideas, and launches.
概览
本地 MCP 服务器,让 AI 助手通过自然对话读取和修改 ProductPlan 的路线图、条目、OKR、想法和发布计划。
- 可以做什么
- 浏览路线图、泳道、里程碑、图例和路线图评论,并查看单个条目及其子项、评论、关联和链接。列出并查看目标、关键结果、想法、客户、标签、机会和想法表单。列出发布计划、发布分区和发布任务,以及用户和团队。创建、编辑和删除条目、泳道、里程碑、条目关联和链接、OKR、想法、机会和发布项。批量更新、创建和删除条目,每次最多 100 个,先做校验并可选试运行。
- 适用场景
- 适合在 ProductPlan 中工作的产品经理、团队负责人等,用于快速查询、站会或干系人汇报、想法筛选和发布协调,无需在网页界面中逐项点击。
- 运行要求
- 以本地二进制形式在 macOS、Linux 或 Windows 上运行(也可用容器镜像),由 AI 助手通过 stdio 启动。需要 ProductPlan 账号,并通过 PRODUCTPLAN_API_TOKEN 环境变量提供 API 令牌,还需能访问 ProductPlan API 的网络。
安装
在 SourceWeft 中
- 打开 控制台中的 ProductPlan MCP Server,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
ProductPlan MCP Server
[CI] [lint] [CodeScene Average Code Health]
Talk to your roadmaps using AI. Ask questions, create ideas, check OKR progress, and manage launches through natural conversation with Claude, Cursor, or other AI assistants.
What can you do with this?
Instead of clicking through ProductPlan's interface, just ask:
"What's on our Q1 roadmap?"
"Show me all objectives that are behind schedule"
"Create a new idea for mobile app improvements"
"What launches are coming up this month?"
"List all ideas tagged 'customer-request'"
"Color every eFormidling bar with the Customer satisfaction legend"
"Move these ten bars to the Platform lane and tag them Q3"
The AI fetches your real ProductPlan data and responds in seconds. Changes to many bars go through one validated call: every bar is checked before anything is written, and you can ask for a dry run first.
Who is this for?
- Product Managers who want faster access to roadmap data
- Team leads who need quick status updates without context-switching
- Anyone using AI assistants (Claude, Cursor, etc.) who wants ProductPlan integrated into their workflow
No coding required. You'll copy a file and paste some settings.
Quick start (5 minutes)
Step 1: Get your ProductPlan API token
- Log into ProductPlan
- Go to Settings → API (or visit this link directly)
- Copy your API token
Step 2: Download the app
Go to the Releases page and download the right file for your computer:
On Mac/Linux, open Terminal and run these two commands (replace the filename with what you downloaded):
You'll be asked for your password. This is normal.
On Windows:
-
Create a folder for the binary (if it doesn't exist):
-
Move the downloaded
.exeto that folder and rename it: -
Use the full path
C:\Tools\productplan.exein your AI assistant config (shown in Step 3)
Note: You can skip adding to PATH. Just use the full file path in your configuration.
Step 3: Connect to your AI assistant
Pick the tool you use:
Claude Desktop (click to expand)
-
Find your config file:
- Mac:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- Mac:
-
Open it in any text editor and add this (replace
your-tokenwith your actual API token):
Mac/Linux:
Windows:
- Restart Claude Desktop
Claude Code (Terminal)
Add to your config file:
- Mac/Linux:
~/.claude.json - Windows:
%USERPROFILE%\.claude.json
Mac/Linux:
Windows:
Cursor
- Open Cursor
- Go to Settings → MCP Servers
- Add this configuration:
Mac/Linux:
Windows:
Windows users: Use double backslashes (
\\) in the path. This is required because backslash is an escape character in JSON.
VS Code + Cline
- Install the Cline extension
- Open VS Code settings (JSON) and add:
Mac/Linux:
Windows:
VS Code + Continue
- Install the Continue extension
- Add to your config file:
- Mac/Linux:
~/.continue/config.json - Windows:
%USERPROFILE%\.continue\config.json
- Mac/Linux:
Mac/Linux:
Windows:
n8n (Workflow Automation)
- Set environment variable on your n8n instance:
- Add an MCP Client node to your workflow
- Configure:
- Command:
- Mac/Linux:
/usr/local/bin/productplan - Windows:
C:\Tools\productplan.exe
- Mac/Linux:
- Environment Variables:
PRODUCTPLAN_API_TOKEN=your-token
- Command:
- Connect to an AI Agent node
Example workflow: Slack Trigger → AI Agent (with MCP Client) → Slack Response
Step 4: Start asking questions
Open your AI assistant and try:
- "List my ProductPlan roadmaps"
- "What bars are on roadmap [name]?"
- "Show me our OKRs"
- "What ideas are in discovery?"
Real-world use cases
Morning standup prep
"Summarize what changed on our Product Roadmap in the last week"
Stakeholder updates
"List all Q1 objectives and their progress"
Idea triage
"Show me all ideas tagged 'enterprise' that don't have a priority set"
Launch coordination
"What tasks are still incomplete for the January launch?"
Quick lookups
"When is the 'Mobile App v2' bar scheduled to start?"
What ProductPlan data can you access?
List tools take filters, so you can ask for exactly what you need: bars by name, dates, lane, legend or tag, and ideas, opportunities, launches and roadmaps by name or status.
Some things the ProductPlan API itself does not allow, so no tool can do them: creating legends, setting lane colors, writing comments, removing a bar from its container once nested, and creating Azure DevOps or Jira integration links (links made through the API are plain web links).
How it works
Why does this need to run on your computer?
MCP (Model Context Protocol) works through a subprocess model. Your AI assistant doesn't connect to a remote server; it spawns the binary as a local process and communicates via stdin/stdout. This architecture means:
- The binary must exist locally because your AI assistant runs it as a child process
- Your API token stays on your machine, never passing through third-party servers
- Real-time, synchronous communication without network latency between AI and the MCP server
- Works offline for cached data (though ProductPlan API calls still need internet)
When you ask "What's on our Q1 roadmap?", here's what happens:
- Your AI assistant recognizes it needs ProductPlan data
- It sends a structured request to the MCP server process
- The binary translates this into ProductPlan API calls
- ProductPlan returns JSON data
- The binary formats and returns results to your AI
- Your AI presents the answer in natural language
Agent Skills
Pre-built workflow guides that teach AI assistants how to use ProductPlan tools effectively. Each skill targets a specific persona with tailored workflows.
Shared Principles
All skills follow these output conventions:
- No raw JSON - Format responses as readable text and tables
- Human-readable dates - Use "March 2025" or "Q1 2025", not "2025-03-15"
- Summarize large lists - Don't overwhelm with 50 items; offer to expand
Persona-specific variations:
- PM includes
bar_idfor follow-up actions - Leadership leads with executive summary, hides implementation details
- Customer-facing omits internal IDs, lane names, and OKRs entirely
To use a skill, copy the SKILL.md file to your Claude Code skills directory:
Or reference skills directly in your prompts:
"Use the productplan-pm workflow to show me our Q1 roadmap"
Troubleshooting
"Command not found" or "spawn ENOENT"
Your AI assistant can't find the binary. This means:
- Mac/Linux: The file isn't at
/usr/local/bin/productplan, or you forgot to runchmod +x - Windows: The path in your config doesn't match where you saved the
.exe
Fix: Verify the binary exists at the path in your config. Run ls -la /usr/local/bin/productplan (Mac/Linux) or check if C:\Tools\productplan.exe exists (Windows).
Windows path issues
Common mistakes on Windows:
Windows uses backslashes (\) for paths, but JSON treats backslash as an escape character. You must double them (\\) in your config file.
"Invalid API token"
Double-check your token at ProductPlan Settings → API. Tokens can expire or be regenerated. Make sure you copied the full token without extra spaces.
"No roadmaps found"
Your API token only accesses data you have permission to see in ProductPlan. Check that your account has access to the roadmaps you're looking for.
AI assistant doesn't see ProductPlan tools
MCP servers load when your AI assistant starts, not when configs change. After editing your config file, fully quit and restart the application. On Mac, use Cmd+Q (not just closing the window).
A bar's color didn't change
Colors are set by legend name (for example "Committed"), not by an ID. Ask your assistant to list the roadmap's legends first. Versions before 6.0.0 sent a legend_id, which ProductPlan treats as "remove the color"; upgrade if colors keep disappearing.
"unknown argument" errors
Since 6.0.0 the server refuses arguments a tool doesn't have, and suggests the closest valid one ("did you mean legend?"). That usually means the assistant guessed a parameter name; it can retry with the suggestion.
"Permission denied" on Mac/Linux
The binary needs execute permission. Run:
Command line (optional)
You can also use this tool directly in Terminal without an AI assistant:
Optional setting: PRODUCTPLAN_CACHE_TTL controls how long read results are cached in memory (default 60s; 0 turns the cache off). Any change you make through the server clears the cache immediately.
Background info
What is MCP?
Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools. Anthropic created it; other AI providers are adopting it. This server implements MCP so your AI assistant can read and write ProductPlan data.
What is ProductPlan?
ProductPlan is roadmap software used by 4,000+ product teams. It handles roadmaps, OKRs, idea discovery, and launch coordination.
For Developers
Project structure
Build from source
Requires Go 1.26 or newer.
Build for all platforms:
Testing
Run all tests:
Run with coverage:
Run benchmarks:
Run evaluation suite:
Coverage (measured 24-09-2026, go test ./... -cover):
Every production file scores 10.0 in CodeScene Code Health (two type-only files can't be scored).
MCP tool reference
50 tools available: 35 READ tools and 15 WRITE tools (12 action-based manage_* plus 3 bulk_* bar tools):
Read tools:
- Roadmaps:
list_roadmaps,get_roadmap,get_roadmap_bars,get_roadmap_lanes,get_roadmap_milestones,get_roadmap_legends,get_roadmap_comments,get_roadmap_complete - Bars:
get_bar,get_bar_children,get_bar_comments,get_bar_connections,get_bar_links - OKRs:
list_objectives,get_objective,list_key_results,get_key_result - Discovery:
list_ideas,get_idea,list_all_customers,list_all_tags,list_opportunities,get_opportunity,list_idea_forms,get_idea_form - Launches:
list_launches,get_launch,get_launch_sections,get_launch_section,get_launch_tasks,get_launch_task - Admin:
check_status,health_check,list_users,list_teams
Write tools:
- Roadmaps:
manage_bar,manage_lane,manage_milestone - Bar relationships:
manage_bar_connection,manage_bar_link - Bulk bars:
bulk_update_bars,bulk_create_bars,bulk_delete_bars(up to 100 bars per call, validated up front,dry_runsupported) - OKRs:
manage_objective,manage_key_result - Discovery:
manage_idea,manage_opportunity - Launches:
manage_launch,manage_launch_section,manage_launch_task
Example:
Architecture
The server uses a clean layered architecture:
Key interfaces:
Logging format:
Changelog
See CHANGELOG.md for release history and detailed changes.
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License
MIT License - see LICENSE
来源:README.md,提交 6db297c
工具
0版本历史
2- v6.0.0最新Sep 24, 2026
- v5.1.0Sep 16, 2026
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