PM Agent Server

Overview

MCP server with 4 tools to help Product Manager (Asha) make data-driven decisions.

The 4 Tools

1. prioritize_backlog

Ranks backlog items (1-35) by RICE scoring.

• Input: method, max_results, filters
• Output: Ranked items with flags (dependencies, stale, unestimated, no customer signal)

2. analyze_feedback

Extracts themes from 90 customer feedback entries.

• Input: group_by, sentiment_filter, bias_analysis flag
• Output: Themes with customer segments, bias warnings

3. assess_capacity

Calculates real team capacity for a sprint.

• Input: sprint_id, engineer names (optional)
• Output: Team capacity + per-engineer breakdown with warnings
• Formula Discovered: available = (21 - pto_days × 2.1) × (allocation/100) - carry_over

4. map_dependencies

Maps dependency chains for backlog items.

• Input: item_ids, max_depth
• Output: Dependency graph, cycles detected, risk flags

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python server.py

Data Path Contract

The server reads data from PM_AGENT_DATA environment variable:

export PM_AGENT_DATA=/path/to/data
python server.py

Falls back to ./data if env var not set.

Tools Usage

Each tool returns JSON with:

• status: "success" or "error"
• data: Tool-specific output
• message: Error details if status is "error"

Files

• server.py - MCP server entry point
• tools/ - Tool implementations
• data/ - Sample data for local testing
• requirements.txt - Dependencies