ProductPlan MCP Server

ProductPlan MCP Server

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.

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Quick start (5 minutes)

Step 1: Get your ProductPlan API token

1. Log into ProductPlan
2. Go to Settings → API (or visit this link directly)
3. 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):

chmod +x ~/Downloads/productplan-darwin-arm64

sudo mv ~/Downloads/productplan-darwin-arm64 /usr/local/bin/productplan

You'll be asked for your password. This is normal.

On Windows:

1. Create a folder for the binary (if it doesn't exist):

   mkdir C:\Tools

1. Move the downloaded .exe to that folder and rename it:

   move %USERPROFILE%\Downloads\productplan-windows-amd64.exe C:\Tools\productplan.exe

1. Use the full path C:\Tools\productplan.exe in 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)

1. Find your config file:

• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

1. Open it in any text editor and add this (replace your-token with your actual API token):

Mac/Linux:

{

"mcpServers": { "productplan": { "command": "/usr/local/bin/productplan", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

Windows:

{

"mcpServers": { "productplan": { "command": "C:\\Tools\\productplan.exe", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

1. Restart Claude Desktop

Claude Code (Terminal)

Add to your config file:

• Mac/Linux: ~/.claude.json
• Windows: %USERPROFILE%\.claude.json

Mac/Linux:

{

"mcpServers": { "productplan": { "command": "/usr/local/bin/productplan", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

Windows:

{

"mcpServers": { "productplan": { "command": "C:\\Tools\\productplan.exe", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

Cursor

1. Open Cursor
2. Go to Settings → MCP Servers
3. Add this configuration:

Mac/Linux:

{

"productplan": { "command": "/usr/local/bin/productplan", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } }

Windows:

{

"productplan": { "command": "C:\\Tools\\productplan.exe", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } }

Windows users: Use double backslashes (\\) in the path. This is required because backslash is an escape character in JSON.

VS Code + Cline

1. Install the Cline extension
2. Open VS Code settings (JSON) and add:

Mac/Linux:

{

"cline.mcpServers": { "productplan": { "command": "/usr/local/bin/productplan", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

Windows:

{

"cline.mcpServers": { "productplan": { "command": "C:\\Tools\\productplan.exe", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } } }

VS Code + Continue

1. Install the Continue extension
2. Add to your config file:

• Mac/Linux: ~/.continue/config.json
• Windows: %USERPROFILE%\.continue\config.json

Mac/Linux:

{

"mcpServers": [ { "name": "productplan", "command": "/usr/local/bin/productplan", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } ] }

Windows:

{

"mcpServers": [ { "name": "productplan", "command": "C:\\Tools\\productplan.exe", "env": { "PRODUCTPLAN_API_TOKEN": "your-token" } } ] }

n8n (Workflow Automation)

1. Set environment variable on your n8n instance:

   N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true

1. Add an MCP Client node to your workflow
2. Configure:

• Command:
• Mac/Linux: /usr/local/bin/productplan
• Windows: C:\Tools\productplan.exe
• Environment Variables: PRODUCTPLAN_API_TOKEN=your-token

1. 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?"

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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?"

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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).

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How it works

┌─────────────────┐      spawns       ┌─────────────────┐      API calls     ┌─────────────────┐

│ AI Assistant │ ───────────────── │ MCP Server │ ─────────────────▶ │ ProductPlan │ │ (Claude, Cursor)│ ◀───────────────▶ │ (this binary) │ ◀───────────────── │ API │ └─────────────────┘ stdin/stdout └─────────────────┘ JSON data └─────────────────┘ your computer your computer cloud

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:

1. The binary must exist locally because your AI assistant runs it as a child process
2. Your API token stays on your machine, never passing through third-party servers
3. Real-time, synchronous communication without network latency between AI and the MCP server
4. 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:

1. Your AI assistant recognizes it needs ProductPlan data
2. It sends a structured request to the MCP server process
3. The binary translates this into ProductPlan API calls
4. ProductPlan returns JSON data
5. The binary formats and returns results to your AI
6. Your AI presents the answer in natural language

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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_id for 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 directo