ProductNerveCenter
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ProductNerveCenterPrioritize my backlog using RICE scoring"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 |
| Rank backlog items using RICE, value/effort, or customer-signal scoring |
| Extract and rank themes from customer feedback |
| Calculate per-engineer sprint capacity with carry-over and skill-fit checks |
| Trace dependency chains via BFS and surface risks |
Related MCP server: jt-mcp-server
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.jsonPrerequisites
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.txtRunning the Server
stdio mode (for Claude Desktop / evaluation agent)
python3 server.pyHTTP mode (for browser/network clients)
python3 server.py http 8000This 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 |
| Path to the |
|
| Remote MCP server URL for team roster & dependency map |
|
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 8000You'll see:
[MCP] ... WARNING MCP server unreachable (...) — roster and deps will be emptyThe 4 tools will still work using the local JSON files in data/.
Tool Details
prioritize_backlog
Parameter | Type | Default | Description |
| string |
| Scoring method: |
| dict |
| Filter by |
| bool |
| Flag items with unresolved blockers |
analyze_feedback
Parameter | Type | Default | Description |
| dict |
|
|
| string |
|
|
| string |
|
|
| string |
|
|
assess_capacity
Parameter | Type | Default | Description |
| string |
| Target sprint ID (uses latest if null) |
| string |
| Filter by squad name |
| bool |
| Subtract in-progress points from capacity |
| bool |
| Flag skill mismatches on assigned items |
map_dependencies
Parameter | Type | Default | Description |
| list |
| Item IDs to trace (null = all in-progress/planned) |
| bool |
| Include external dependencies |
| int |
| Maximum BFS traversal depth |
This server cannot be installed
Maintenance
Resources
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