Supabase
MCP server for interacting with the Supabase platform
Beschreibung vom Entwickler (englisches Original)
Project tracker for AI agents: roadmap, periods, tasks and a validation gate, in one SQLite file
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.
AIRA_DATA_DIRDu 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": {
"fronyboard": {
"command": "uvx",
"args": [
"fronyboard"
],
"env": {
"AIRA_DATA_DIR": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add fronyboard --env AIRA_DATA_DIR=your-value -- uvx fronyboardTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"fronyboard": {
"command": "uvx",
"args": [
"fronyboard"
],
"env": {
"AIRA_DATA_DIR": "your-value"
}
}
}
}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": {
"fronyboard": {
"type": "stdio",
"command": "uvx",
"args": [
"fronyboard"
],
"env": {
"AIRA_DATA_DIR": "your-value"
}
}
}
}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.
AIRA_DATA_DIR envDiese 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 8. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto Cafelatte1 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 (0.43.0) kam vor 9 Tagen 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: Supabase, MongoDB MCP Server, Elasticsearch MCP Server
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
MCP server for interacting with the Supabase platform
MongoDB Model Context Protocol Server
MCP server for interacting with Elasticsearch
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.
An MCP server that gives AI agents (Claude Code and friends) a first-class project tracker.
Where Jira is an issue tracker for humans behind a web UI, FronyBoard replaces each part with something an agent can use natively:
| Jira | FronyBoard |
|---|---|
| Database | One SQLite file in a dedicated data directory |
| Records | JSON documents (roadmap, period) + Markdown |
| API | MCP tools |
| Workflow engine | Schema + rule validation, run as a gate before every write |
| State transition | An MCP tool call (transition_task) |
The schema and operating rules were extracted from a real product's management system (31 tasks shipped through it), then generalized.
Requires uv. One line registers FronyBoard in Claude Code; uvx fetches the package from PyPI on first use and caches it:
claude mcp add FronyBoard -- uvx fronyboard
Any MCP client that can launch a stdio command works the same way — the command is uvx fronyboard. It is also listed in the MCP Registry as io.github.Cafelatte1/fronyboard. From a clone, point at the checkout instead (this needs git):
git clone https://github.com/Cafelatte1/fronyboard
claude mcp add FronyBoard -- uv run --directory \backend fronyboard
This is the local (stdio) mode: the client starts the server as a child process and talks to it over a pipe. No HTTP, no network, no credentials — web.py and fauth.py are never called. Data is written to %LOCALAPPDATA%\Frony\FronyBoard\data (~/.Frony/FronyBoard/data where LOCALAPPDATA is unset); set AIRA_DATA_DIR to relocate it. Logs (JSON Lines, one line per MCP tool call plus server events) go to the sibling logs folder — FRONYBOARD_LOG_DIR overrides.
To share one FronyBoard between several machines, or to use it from the Claude and ChatGPT apps, run it as an HTTP server instead — see docs/self-hosting.md.
Connecting the MCP server gives every session the tools and the general workflow (delivered as server instructions). What it cannot know is which FronyBoard project a codebase belongs to — declare that in the codebase itself by adding this section to its CLAUDE.md (create the file if the project has none):
## FronyBoard
This project is tracked by FronyBoard (project key: DLY). Manage tasks through the FronyBoard MCP tools, following the FronyBoard server instructions.
Replace DLY with the project's key (register one first with create_project). The section is also the opt-in signal: a codebase without it is treated as not FronyBoard-managed.
fronyboard.db
├── projects one row per project (key e.g. DLY): the roadmap record — │ yearly overview (goal / now / target / checklist) + quarterly milestones └── periods one row per opened period (e.g. 2026Q3): tasks ({KEY}-001, ...)
result (retrospective, written when the period closes)
A project is two kinds of records — the roadmap, and one record per period. Both are JSON documents; the schema and the rules that guard it are in backend/src/fronyboard/validation.py.
DLY-042) — they keep counting across periods and are never reused. They are the only link between FronyBoard and a codebase: use them in branch names (feat/DLY-042/short-desc) and record the branch on the task.depends_on, branch and two one-line notes of at most 300 characters each. Agents read titles and status; the 25-line body nobody read, and the month/week slots that only existed to schedule it, are gone. Time is meta.completed_at.content is written at create time and says why the task exists — the pressure behind it, or the scope deliberately left out. The approach chosen is not part of it (AIR-092): agents wrote it here and again in the commit, so it lives in the commit only. check is required by transition_task(status="done") and says what proves it done — the command and its output, or an observable a reader can go and see. One free note asked before the work can only restate the plan: in a 12-session benchmark every task an agent wrote paraphrased its own title, because the note was fixed at create time and update_task was never called. Reopening a task drops its check; nothing is proven any more.period → roadmap milestone (the quarter). That is the only one, and the milestone is optional (v0.40.0, AIR-095): a period without one is valid, and its tasks are checked like any other's.planned | active | done; tasks: todo | in_progress | done | blocked | cancelled.list_tasks takes include_cancelled). blocked = may resume, cancelled = will not happen; transitioning a cancelled task restores it.recent_done: the ten most recently finished tasks with their content, their check and when they completed. What is already built is what a cold session needs most, and nobody makes a second call to find it (AIR-090). It is still a record of claims: the board says what an agent reported, the code is the evidence.follows until v0.37.0; the boot migration renames it. It is a pointer, not a lock: list_tasks and get_status flag the entries not yet done as waiting_on, and nothing is ever blocked.meta.created_at / updated_at / started_at / completed_at) are stamped by the server in naive UTC — started_at on the first in_progress transition, completed_at on done (and removed again if the task leaves done). Agents never write them.| Area | Tools |
|---|---|
| Projects | create_project, update_project, list_projects, get_roadmap, get_status, validate |
| Roadmap | set_overview, set_check, upsert_milestone |
| Periods | open_period, close_period, get_retrospective |
| Planning | create_task, update_task, transition_task |
| Queries | list_tasks, get_task, search_tasks, recent_activity |
| Guide | help — the full usage guide as Markdown; the server instructions point to it |
update_task and transition_task derive the project from the task id prefix (DLY-042 → DLY), so their key parameter is optional. Re-calling close_period on a closed period rewrites its retrospective.
Typical flow:
create_project
→ create_task (opens the current quarter) → transition_task in_progress (with branch) → ... → transition_task done → close_period (retrospective)
Planning is optional, not a prerequisite. create_task opens the current quarter by itself (in FRONYBOARD_TZ, else the server's zone); open_period opens any other quarter, with or without a milestone. set_overview and upsert_milestone add the year's and the quarter's goals whenever there are some. A milestone added to an open quarter starts active; closing the quarter marks it done.
Every mutation is validated before anything is written; invalid changes are rejected with the full error list. close_period refuses while tasks are still todo or in_progress. Writes are serialized per project, so concurrent clients cannot collide on ids or lose updates.
The same package also runs as an always-on HTTP server (fronyboard serve): MCP over streamable HTTP for every machine on your network, a read-only web dashboard for humans, API keys per device, and OAuth for the hosted Claude / ChatGPT apps. Authentication is delegated to FronyAuth, a separate service. None of it is needed for the stdio install above. To see the dashboard on your own machine without any of that, run fronyboard serve --local (loopback only, no login) and open http://127.0.0.1:8642. docs/self-hosting.md covers the setup; docs/operations.md is the day-2 runbook.
uv run --directory backend pytest # backend
cd frontend; npm test # dashboard
The repo is a monorepo. backend/src/fronyboard/ — store.py (SQLite, data root), validation.py (schema gate), service.py (operations), auth.py (bearer middleware) + fauth.py (FronyAuth client), log.py, web.py (JSON API + static serving), server.py (MCP tool surface + CLI). frontend/ — the dashboard (React + Vite), built to static files that the backend serves; its build output frontend/dist is committed so a server needs no Node toolchain.
The three docs under docs/ cover what the code cannot tell you — running this on your own machines: