PM Copilot

An MCP server that triangulates customer support tickets and feature requests to help PMs decide what to build next.

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Real results: Analyzed 2,370 signals (2,136 support tickets + 234 feature requests) across 3 products in 55 seconds. Identified 16 themes, 15 convergent. Top priority: Booking & Scheduling (score: 134.6) — 629 tickets + 77 feature requests pointing at the same problem.

Read the full story: I built an MCP server that changed how I prioritize products — why I built this, how convergent signals work in practice, and what I learned building with Claude Code.

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What Makes This Different

• Signal triangulation — Not just data access. Matches support tickets against feature requests to find convergent themes, then scores them with a weighted formula that gives convergent signals a 2x priority boost.
• Composability — Designed to work with other MCP servers. Pass churn data from Metabase or traffic trends from Google Analytics into generate_product_plan via kpi_context, and the methodology adjusts priorities accordingly.
• PM methodology built in — Opinionated scoring based on 7 years of real product management across 9 products and 1M+ users. Not a generic framework — an actual decision-making process exposed as an MCP resource.
• PII scrubbing — Customer data never reaches the LLM unfiltered. SSNs, credit cards (Luhn-validated), emails, and phone numbers are redacted before analysis. Agent responses are filtered out of quotes.

Architecture

graph TD
    A[Claude Desktop / Code] -->|stdio| B[pm-copilot]
    A -->|stdio| C[Metabase MCP]
    A -->|stdio| D[Google Analytics MCP]
    B -->|Qualitative| E[HelpScout: tickets]
    B -->|Qualitative| F[ProductLift: feature requests]
    C -->|Quantitative| G[Conversion, Churn, Revenue]
    D -->|Acquisition| H[Traffic, Channels, Trends]
    B -.->|kpi_context| A

Claude orchestrates multiple MCP servers. PM Copilot handles qualitative customer signals. Other servers provide quantitative business metrics. The kpi_context parameter is the integration point — no point-to-point integrations required.

Quick Start

git clone https://github.com/dkships/pm-copilot.git
cd pm-copilot
npm install
cp .env.example .env   # Edit with your credentials
npm run build

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pm-copilot": {
      "command": "node",
      "args": ["/absolute/path/to/pm-copilot/dist/index.js"]
    }
  }
}

Claude Code

claude mcp add pm-copilot -- node /absolute/path/to/pm-copilot/dist/index.js

Or use the .mcp.json already in the project root — Claude Code picks it up automatically.

Tools

synthesize_feedback

Cross-references HelpScout tickets and ProductLift feature requests, returns theme-matched analysis with priority scores.

Parameter	Type	Default	Description
timeframe_days	number	30	Days to look back (1-90)
top_voted_limit	number	50	Max feature requests by vote count
mailbox_id	string	—	HelpScout mailbox filter
portal_name	string	—	ProductLift portal filter
detail_level	string	"summary"	"summary" (~19KB), "standard" (~68KB), or "full" (~563KB)

Returns themes sorted by priority score, each with reactive/proactive counts, convergence flag, evidence summaries, and representative customer quotes.

generate_product_plan

Builds a prioritized product plan with evidence and customer quotes. Accepts external business metrics via kpi_context.

Parameter	Type	Default	Description
timeframe_days	number	30	Days to look back (1-90)
top_voted_limit	number	50	Max feature requests by vote count
mailbox_id	string	—	HelpScout mailbox filter
portal_name	string	—	ProductLift portal filter
kpi_context	string	—	Business metrics from other MCP servers
max_priorities	number	5	Number of priorities to return (1-10)
preview_only	boolean	false	Audit mode: show what data would be sent
detail_level	string	"summary"	"summary" (~7KB), "standard" (~21KB), or "full" (~584KB)

get_feature_requests

Raw ProductLift data access for browsing feature requests directly.

Parameter	Type	Default	Description
portal_name	string	—	Filter to a specific portal
include_comments	boolean	true	Include comments on each request

Composability in Action

PM Copilot is designed to work alongside other MCP servers. Here's a real example using live data from 3 AppSumo Originals products.

Step 1: The PM asks a single question

Pull our churn and booking completion data, then use pm-copilot to create a product plan using all of that context.

Step 2: pm-copilot analyzes 10,424 signals and returns the top priorities

#	Theme	Score	Tickets	Feature Requests	Signal
1	Billing & Payment	91.1	2,336	20	Convergent
2	Booking & Scheduling	87.1	682	74	Convergent
3	Account & Licensing	69.7	1,955	8	Convergent
4	Team & Collaboration	64.4	1,875	19	Convergent
5	Whitelabel & Branding	50.2	92	30	Convergent

Step 3: Business metrics from dashboards arrive as kpi_context

TidyCal: booking completion rate dropped from 74% to 66% over last
30 days. Monthly churn increased from 3.1% to 4.2%. Organic traffic
up 22% MoM. BreezeDoc: document completion rate steady at 81%.
Churn flat at 2.8%.

Step 4: Claude synthesizes both — and overrides the formula

The scores say Billing & Payment is #1. But the methodology says churn data overrides the formula. With TidyCal's booking completion dropping 8 points and churn spiking 35%, Booking & Scheduling becomes the real #1 — it's the core product breaking.

BreezeDoc deprioritized (stable metrics, no fire). TidyCal's 22% organic traffic growth elevates Whitelabel & Branding as a growth play.

The server provides the signal ranking. KPI context provides the override judgment. Claude synthesizes both.

Methodology

PM Copilot exposes a pm-copilot://methodology resource — David Kelly's actual product planning framework built over 7 years of launching 9 products to 1M+ users at AppSumo Originals.

Key principles:

• The 5% rule — You complete ~5% of what customers ask for each month. The framework identifies which 5% matters most.
• Convergent signals always win — Same theme in both support tickets AND feature requests = highest confidence signal.
• Reactive > proactive — Broken stuff drives churn. You can survive not having a feature; you can't survive errors.
• Business metrics override the formula — Rising churn, dropping conversion, or revenue impact can change everything.

The methodology is versioned (v2.0) and served as markdown content via the MCP resource protocol. Claude references it automatically when using generate_product_plan.

Security

Customer data flows through PM Copilot on its way to Claude. All text is scrubbed before it enters the analysis pipeline or leaves the server.

PII scrubbing

Category	Method	Replacement
SSNs	Pattern match (XXX-XX-XXXX)	[SSN REDACTED]
Credit cards	13-19 digit sequences + Luhn validation	[CREDIT CARD REDACTED]
Email addresses	Standard email pattern	[EMAIL REDACTED]
Phone numbers	US formats (+1, parens, dashes, dots)	[PHONE REDACTED]
Customer email field	Always redacted	[REDACTED]

What we exclude entirely

Data	Why
Agent/admin responses	Only customer voice matters; agent replies could leak internal process
Internal HelpScout notes	May contain credentials, workarounds, internal discussions
Attachments	Could contain screenshots with PII, invoices, medical documents
Voter identities	Vote counts are sufficient; individual identity adds no PM value

Audit controls

• preview_only: true on generate_product_plan shows what data would be sent without fetching it
• Every response includes pii_scrubbing_applied and pii_categories_redacted metadata
• Data categories logged to stderr on each call (categories only, never content)

Development

npm install          # Install dependencies
npm run build        # Compile TypeScript
npm run dev          # Watch mode
npm start            # Run the server

Theme configuration

themes.config.json in the project root defines what themes to look for. Edit without rebuilding — loaded at runtime.

Ships with 16 data-driven themes across 9 categories. Add your own by appending to the themes array. Unmatched data points are analyzed for emerging patterns using bigram/trigram frequency detection.

Scoring formula

priority = (frequency × 0.35 + severity × 0.35 + vote_momentum × 0.30) × convergence_boost

• Frequency (0.35): Count of data points, normalized across themes
• Severity (0.35): Reactive signals only — thread depth, recency (7-day half-life decay), tag boosts
• Vote momentum (0.30): Proactive signals only — 80% votes + 20% comments
• Convergence (2x): Applied when a theme has both reactive and proactive signals

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Ensure npm run build succeeds with no errors
4. Follow existing patterns: tools use registerTool, API clients get their own module, PII scrubbing happens at the format layer
5. Open a pull request

License

MIT