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Quarterback

by bobbyrgoldsmithversion 1.0.1i★ –iRuns on your computer
Registry nameio.github.bobbyrgoldsmith/quarterback

Install

Overview

Description from the maker (original English)

Strategic task prioritization and agent orchestration for multi-project operators

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Docs, sheets & office work

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "quarterback": {
      "command": "uvx",
      "args": [
        "quarterback"
      ]
    }
  }
}

Run this in your terminal

claude mcp add quarterback -- uvx quarterback

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "quarterback": {
      "command": "uvx",
      "args": [
        "quarterback"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "quarterback": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "quarterback"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account bobbyrgoldsmith, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Docs, sheets & office worki

  • More stars than 0% of the other 2196 servers in this category
  • Published to the registry more recently than 5% of the other 2196 servers in this category
  • Basic checks equal to or better than 100% of the other 2196 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Quarterback safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.1) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Agent-Native Google Calendar - manage events, sync, and public booking

Runs in the cloud★ 7,620MITUpdated 2 months ago
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Excel MCP Server

by haris-musa

Lets AI create and edit Excel files itself, including formulas, charts and PivotTables, without Excel installed.

Runs on your computer★ 4,276MITUpdated 6 days ago
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Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

Runs on your computer★ 847MITUpdated 4 days ago
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Quarterback

Read the field. Call the play.

Strategic task prioritization and agent orchestration for multi-project operators.


Every other AI task manager breaks down one project into subtasks. Quarterback helps you decide which of your ten projects to prioritize right now — using a 5-factor weighted scoring engine, organizational context, and time-aware planning. It runs locally, costs nothing, and works as both a standalone CLI and an MCP server for Claude.

What Makes Quarterback Different

FeatureQuarterbackTaskMaster AIShrimp Task Manager
Multi-project prioritization5-factor weighted engineSingle-project breakdownSingle-project
Advisory document systemAnalyze articles against your goalsNoNo
Agent orchestrationAutonomy levels + webhooksNoNo
Time-aware planningWorking hours, lunch, buffer timeNoNo
Organizational contextGoals, constraints, workflowsNoNo
Knowledge wiki (Playbook)LLM-maintained wiki for cross-session consistencyNoNo
Conflict detectionCross-project scheduling conflictsNoNo
Standalone CLIFull CLI without AI runtimeRequires AIRequires AI
CostFree (MIT)FreeFree

Quick Start

# Install

pip install quarterback

Initialize (creates ~/.quarterback/)

quarterback init

Interactive setup wizard — walks you through org, goals, workflows, projects, constraints

quarterback setup

Add your first project and tasks

quarterback add "Launch landing page" --project "My Startup" --priority 4 --effort 3 --impact 5 quarterback add "Write blog post" --project "Content" --priority 3 --effort 2 --impact 3

See what to work on

quarterback priorities

Find quick wins

quarterback quick-wins

Plan your day with time awareness

quarterback plan-day

LLM-Powered Setup (via MCP)

When using Quarterback as an MCP server, ask your LLM: *"Set up Quarterback for me"* — it will call the setup_quarterback tool, interview you conversationally about your business, goals, workflows, projects, constraints, and knowledge base (Playbook), then write all config files and database records in one shot. No manual YAML editing required.

MCP Server

Quarterback works with any MCP-compatible client — Claude Desktop, Claude Code, Cursor, Windsurf, Cline, OpenAI agents, and others. All 23 tools use standard MCP protocol (JSON-RPC over stdio) with no LLM-specific dependencies.

# Install with MCP support

pip install quarterback[mcp]

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{

"mcpServers": { "quarterback": { "command": "quarterback-server" } } }

Or for Claude Code (~/.claude/settings.json):

{

"mcpServers": { "quarterback": { "command": "quarterback-server" } } }

The same quarterback-server command works with any MCP client — just add it to your client's server configuration.

Then ask your LLM: *"What should I work on today?"* — it will use all 23 Quarterback tools to analyze your priorities.

Features

5-Factor Prioritization Engine

Every task is scored across five dimensions:

FactorWeightWhat it measures
Impact30%Task impact + project revenue/strategic value
Urgency25%Due date proximity + blocking status
Strategic25%Project priority + milestone status
Effort15%Inverted effort score (quick tasks score higher)
Quick Win5%High impact + low effort bonus
Advisory Document System

Analyze external articles, books, and advice against your organizational context:

# Import and auto-analyze an article

quarterback advisory-add --title "Growth Strategy" --url https://example.com/article

Review the analysis

quarterback advisory-view --id 1

Approve recommendations (optionally create tasks)

quarterback advisory-approve --id 1 --approve 1,3,5 --create-tasks

The analyzer checks every recommendation against your goals and constraints, flagging conflicts and synergies.

Agent Orchestration

Mark tasks for autonomous agent execution with configurable autonomy:

  • Draft: Agent creates a draft for your review
  • Checkpoint: Agent pauses at key decisions for approval
  • Autonomous: Agent runs to completion

Webhooks notify your automation layer (n8n, Zapier, custom) when tasks are ready.

Playbook — Knowledge Wiki

Playbook is Quarterback's compiled knowledge layer. It's an LLM-maintained markdown wiki that gives every session — local CLI, MCP, or autonomous agent — the same canonical context about your projects, decisions, and strategies.

The problem it solves: Without Playbook, each AI session starts fresh and re-derives your organizational context from sparse signals. Two sessions running the same query can produce different results because they reconstruct understanding independently. Playbook provides accumulated knowledge that all sessions read from.

How it works:

~/playbook/                          (or ~/.quarterback/playbook/)

├── CLAUDE.md # Schema — how the LLM reads/writes pages ├── raw/ # Drop zone for source material └── wiki/ ├── index.md # Master catalog — read this first ├── entities/ # Companies, products, clients, tools ├── concepts/ # Patterns, strategies, recurring themes ├── decisions/ # Decisions with rationale and alternatives ├── compiled/ # QB-compatible files for task scoring │ ├── goals.md # Read by QB's prioritization engine │ └── constraints.md # Read by QB's conflict detection └── log.md # Append-only operations record

Setup: Playbook is created automatically during quarterback setup (or the MCP setup wizard). The interview asks about your key entities, concepts, and decisions, then seeds initial wiki pages.

Without Playbook: Quarterback works exactly as before — reading goals and constraints from ~/.quarterback/org-context/ files. Playbook is opt-in.

With Playbook: Quarterback reads compiled/goals.md and compiled/constraints.md from Playbook first, falling back to org-context/ files if Playbook isn't initialized. Your LLM reads wiki/index.md for full organizational context.

# Check Playbook status

quarterback playbook status

Browse the index

quarterback playbook index

List pages by category

quarterback playbook list --category entities

Read a specific page

quarterback playbook read entities/my-product.md

Search across all pages

quarterback playbook search "budget"

Obsidian integration (optional): During setup, you can opt to configure Playbook as an Obsidian vault. Open the Playbook folder in Obsidian for graph visualization and visual editing. Install an Obsidian MCP server for programmatic access. No Obsidian dependency required — Playbook works as plain markdown files.

CI/CD Pipeline Integration

Quarterback's CLI and webhook system make it a natural fit for automated pipelines — update task status, log deliverables, and trigger downstream work without a human in the loop.

Direct CLI in pipelines

Add Quarterback commands to any CI/CD step. The CLI is stateless and scriptable:

# GitHub Actions example: auto-update task on deploy
  • name: Mark deploy task complete run: | pip install quarterback export QUARTERBACK_HOME=${{ runner.temp }}/.quarterback quarterback update 42 --status completed --notes "Deployed via CI, SHA: ${{ github.sha }}"
# After test suite passes, log results to a task
  • name: Report test results run: | quarterback update 38 --notes "Tests passed: 106/106, coverage 87%. Build #${{ github.run_number }}"
# Nightly: check for overdue deliverables and alert
  • name: Nightly priority check run: | quarterback alert-check quarterback priorities today --limit 5
Agentic CI/CD with webhooks

Register a webhook and let your automation layer react to task events in real time:

# Register a webhook pointing at your n8n/Zapier/custom endpoint

quarterback-server # MCP tools available, or use CLI:

# In your automation script: mark a task agent-ready after PR merge

import subprocess subprocess.run([ "quarterback", "update", "55", "--status", "completed", "--notes", f"PR #{pr_number} merged. Deployed to staging." ])

Use cases:

Pipeline eventQuarterback actionWhat happens
PR mergedupdate_task status=completedTask marked done, webhook fires to Slack
Deploy succeedsupdate_task with SHA + environment notesDeliverable tracked with audit trail
Nightly cronget_priorities + alert-checkTeam gets daily summary of what's overdue
Test suite failsadd_task with failure detailsBug auto-filed, linked to project
Sprint startsget_priorities + detect_conflictsSurface scheduling conflicts before work begins
Agent completes workupdate_agent_status status=completedWebhook notifies orchestrator, next task dispatched
Release taggedadvisory-add with release notesChangelog analyzed against project goals
Shared database across environments

Point multiple environments at the same Quarterback instance:

# All CI runners share one database via mounted volume or network path

export QUARTERBACK_HOME=/shared/quarterback

Or per-environment with migration

quarterback migrate /path/to/source

This lets your local CLI, CI pipelines, and MCP-connected agents all read and write to the same task graph — giving you a single source of truth across manual and automated workflows.

Time-Aware Planning
quarterback plan-day

Considers your working hours, lunch break, buffer time for meetings, and current time to suggest tasks that actually fit in your remaining day.

Configuration

Organizational Context

After quarterback init, run quarterback setup for an interactive wizard, or ask Claude to run the setup wizard via MCP. You can also manually configure your context in ~/.quarterback/org-context/:

~/.quarterback/org-context/

├── goals.md # Your strategic, workflow, and project goals ├── projects.yaml # Active projects with metadata ├── workflows.yaml # Groups of related projects └── constraints.md # Time, budget, and strategic boundaries

Example templates are included — copy from .example files and customize.

If you enable Playbook during setup, goals.md and constraints.md are auto-maintained from the wiki's compiled/ directory. You can still manually edit the org-context files — Playbook is additive, not required.

Alert Configuration

Configure notifications in ~/.quarterback/config/alerts.yaml:

  • Quiet hours (no notification

Only the beginning is shown; read the rest in the repo

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