ProductNerveCenter

An MCP (Model Context Protocol) server that exposes product-management tools for AI agents. Built with the MCP Python SDK using FastMCP.

Tools

Tool	Description
prioritize_backlog	Rank backlog items using RICE, value/effort, or customer-signal scoring
analyze_feedback	Extract and rank themes from customer feedback
assess_capacity	Calculate per-engineer sprint capacity with carry-over and skill-fit checks
map_dependencies	Trace dependency chains via BFS and surface risks

Project Structure

ProductNerveCenter/
├── server.py                      # MCP server — data loading + tool wrappers
├── olympics.json                  # Evaluation agent configuration
├── tools/
│   ├── __init__.py                # Package exports
│   ├── prioritize_backlog.py      # RICE / value-effort / customer-signal scoring
│   ├── analyze_feedback.py        # Theme extraction & grouping logic
│   ├── assess_capacity.py         # Sprint capacity calculation
│   └── map_dependencies.py        # BFS dep traversal & risk analysis
├── data/
│   ├── product_backlog.json       # 35 backlog items
│   ├── customer_feedback.json     # 90 customer feedback entries
│   ├── team_roster.json           # 8 engineers across 2 squads
│   ├── dependencies.json          # Dependency graph edges
│   └── sprint_history.json        # 6 sprint history records
├── oracle_connection/
│   └── README.md                  # Discovery process documentation
├── TECHNICAL_DECISIONS.md         # Design decisions log
├── data_dictionary.md             # Field definitions for all data files
├── requirements.txt
├── agent_config.json
└── env_vars.json

Prerequisites

• Python 3.11 or 3.12
• pip

Setup

# Set Python version (if using pyenv)
pyenv local 3.11.11  # or 3.12.3

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Running the Server

stdio mode (for Claude Desktop / evaluation agent)

python3 server.py

HTTP mode (for browser/network clients)

python3 server.py http 8000

This starts a Streamable HTTP server at http://127.0.0.1:8000.

Connecting to the Server

From Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "devpulse": {
      "command": "python3",
      "args": ["/full/path/to/ProductNerveCenter/server.py"],
      "env": {
        "PM_AGENT_DATA": "/full/path/to/ProductNerveCenter/data",
        "MCP_DATA_URL": "https://co-mcp-server-dev.apps-internal.lrl.lilly.com/mcp"
      }
    }
  }
}

From another MCP client (HTTP mode)

from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

async with streamablehttp_client("http://127.0.0.1:8000/mcp") as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        result = await session.call_tool("prioritize_backlog", {"method": "rice"})
        print(result)

Environment Variables

Variable	Purpose	Default
PM_AGENT_DATA	Path to the data/ folder with JSON files	./data
MCP_DATA_URL	Remote MCP server URL for team roster & dependency map	https://co-mcp-server-dev.apps-internal.lrl.lilly.com/mcp

Quick Test (no remote server needed)

The server gracefully handles the remote MCP server being unreachable — it logs a warning and continues with empty roster/deps. You can run it locally right away:

source .venv/bin/activate
python3 server.py http 8000

You'll see:

[MCP] ... WARNING MCP server unreachable (...) — roster and deps will be empty

The 4 tools will still work using the local JSON files in data/.

Tool Details

prioritize_backlog

Parameter	Type	Default	Description
method	string	"value_effort"	Scoring method: "rice", "value_effort", "customer_signal"
filters	dict	null	Filter by squad, status, or tags
include_dependency_check	bool	true	Flag items with unresolved blockers

analyze_feedback

Parameter	Type	Default	Description
time_range	dict	null	{"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
customer_tier	string	null	"enterprise", "mid_market", or "startup"
source	string	null	"support_ticket", "nps_survey", "sales_call", "user_interview"
group_by	string	"theme"	"theme", "customer", or "source"

assess_capacity

Parameter	Type	Default	Description
sprint_id	string	null	Target sprint ID (uses latest if null)
squad	string	"all"	Filter by squad name
include_carry_over	bool	true	Subtract in-progress points from capacity
check_skill_fit	bool	false	Flag skill mismatches on assigned items

map_dependencies

Parameter	Type	Default	Description
item_ids	list	null	Item IDs to trace (null = all in-progress/planned)
include_external	bool	true	Include external dependencies
max_depth	int	3	Maximum BFS traversal depth