Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Beschreibung vom Entwickler (englisches Original)
Strategic task prioritization and agent orchestration for multi-project operators
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"quarterback": {
"command": "uvx",
"args": [
"quarterback"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add quarterback -- uvx quarterbackIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"quarterback": {
"command": "uvx",
"args": [
"quarterback"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"quarterback": {
"type": "stdio",
"command": "uvx",
"args": [
"quarterback"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto bobbyrgoldsmith bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (1.0.1) kam vor 8 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.
| Feature | Quarterback | TaskMaster AI | Shrimp Task Manager |
|---|---|---|---|
| Multi-project prioritization | 5-factor weighted engine | Single-project breakdown | Single-project |
| Advisory document system | Analyze articles against your goals | No | No |
| Agent orchestration | Autonomy levels + webhooks | No | No |
| Time-aware planning | Working hours, lunch, buffer time | No | No |
| Organizational context | Goals, constraints, workflows | No | No |
| Knowledge wiki (Playbook) | LLM-maintained wiki for cross-session consistency | No | No |
| Conflict detection | Cross-project scheduling conflicts | No | No |
| Standalone CLI | Full CLI without AI runtime | Requires AI | Requires AI |
| Cost | Free (MIT) | Free | Free |
# Install
pip install quarterback
quarterback init
quarterback setup
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
quarterback priorities
quarterback quick-wins
quarterback plan-day
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.
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.
Every task is scored across five dimensions:
| Factor | Weight | What it measures |
|---|---|---|
| Impact | 30% | Task impact + project revenue/strategic value |
| Urgency | 25% | Due date proximity + blocking status |
| Strategic | 25% | Project priority + milestone status |
| Effort | 15% | Inverted effort score (quick tasks score higher) |
| Quick Win | 5% | High impact + low effort bonus |
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
quarterback advisory-view --id 1
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.
Mark tasks for autonomous agent execution with configurable autonomy:
Webhooks notify your automation layer (n8n, Zapier, custom) when tasks are ready.
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
quarterback playbook index
quarterback playbook list --category entities
quarterback playbook read entities/my-product.md
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.
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.
Add Quarterback commands to any CI/CD step. The CLI is stateless and scriptable:
# GitHub Actions example: auto-update task on deploy
# After test suite passes, log results to a task
# Nightly: check for overdue deliverables and alert
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 event | Quarterback action | What happens |
|---|---|---|
| PR merged | update_task status=completed | Task marked done, webhook fires to Slack |
| Deploy succeeds | update_task with SHA + environment notes | Deliverable tracked with audit trail |
| Nightly cron | get_priorities + alert-check | Team gets daily summary of what's overdue |
| Test suite fails | add_task with failure details | Bug auto-filed, linked to project |
| Sprint starts | get_priorities + detect_conflicts | Surface scheduling conflicts before work begins |
| Agent completes work | update_agent_status status=completed | Webhook notifies orchestrator, next task dispatched |
| Release tagged | advisory-add with release notes | Changelog analyzed against project goals |
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
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.
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.
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.
Configure notifications in ~/.quarterback/config/alerts.yaml:
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