ProductNerveCenter
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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 |
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
Maintenance
Related MCP Connectors
MCP server for building and testing AI agents with multi-model experimentation and insights.
Your product team's shared strategic memory — an MCP server your AI tools reason over.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
Related MCP Servers
- AlicenseAqualityDmaintenanceMCP server exposing 40 best-practice product management skills plus workflows and lifecycle tools, instantly accessible to any AI via Model Context Protocol.1993 npm21Apache 2.0
- AlicenseNot gradedqualityDmaintenanceMCP server that equips AI agents with dev workflow tools including GitHub project management, conventional commits, visual regression testing, Jira/Confluence integration, and a persistent memory knowledge graph.11 npmMIT
- AlicenseNot gradedqualityDmaintenanceMCP server for managing a project backlog as Markdown files in Git, enabling AI agents to read, create, and update tasks programmatically.2MIT
- AlicenseNot gradedqualityDmaintenanceA comprehensive MCP server for managing product initiatives, architecture decisions, requirements, and releases, enabling AI tools to interact with architectural documentation and planning.MIT