An MCP server that gives AI agents a first-class project tracker
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
claude mcp add fronyboard -- uvx fronyboard{
"mcpServers": {
"fronyboard": {
"command": "uvx",
"args": ["fronyboard"]
}
}
}MCP Servers overview
# FronyBoard
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.
## Install
Requires [uv](https://docs.astral.sh/uv/). One line registers FronyBoard in Claude Code;
`uvx` fetches the package from PyPI on first use and caches it:
```powershell
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](https://registry.modelcontextprotocol.io) as `io.github.Cafelatte1/fronyboard`.
From a clone, point at the checkout instead (this needs `git`):
```powershell
git clone https://github.com/Cafelatte1/fronyboard
claude mcp add FronyBoard -- uv run --directory <path-to-clone>\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; see
[docs/logging.md](docs/logging.md).
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](docs/self-hosting.md).
## Project setup
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):
```markdown
## 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.
## Model
```
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): monthly milestones (M1, M2, ...)
+ 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 shapes are in [docs/data-model.md](docs/data-model.md).
- **Task ids are a project-global sequence** (`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.
- **Reference chain**: `task.month → months[].id`, `period file → roadmap milestone`.
Rollups follow this chain — months are the grouping unit.
- **Statuses** — milestones and months: `planned | active | done`;
tasks: `todo | in_progress | done | blocked | cancelled`.
- **`cancelled` is the soft delete** — there is no hard delete. Cancelling requires a
reason, keeps the record (and its id) forever, and hides the task from queries by
default (`list_tasks` takes `include_cancelled`). `blocked` = may resume,
`cancelled` = will not happen; transitioning a cancelled task restores it.
- **Carry-over**: a task that outlives its period is not moved — recreate it in the next
period under a new id and note the mapping in the closing retrospective.
- **`after`** on a task lists the tasks it continues from (other projects allowed). It is a
pointer, not a lock: `get_task` shows the reverse as `followed_by`, `list_tasks` flags
`waiting_on` while predecessors are open, and nothing is ever blocked.
- **Timestamps** (`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.
- **The `result` field closes a period** — the rest of the file holds only current
state, so the "why it turned out this way" lives there: judgment and reasons,
not counts. Its presence is what marks a period closed.
## Tools
| 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 | `upsert_month`, `create_task`, `update_task`, `transition_task` |
| Queries | `list_tasks`, `get_task`, `search_tasks`, `recent_activity` |
`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.
The two `upsert_*` tools sit at different levels: `upsert_milestone` is a quarter
in the roadmap, `upsert_month` is one of the three months inside a period that is
already open. A task's `month` is a month id (`M1`/`M2`/`M3`), never `YYYY-MM`.
Typical flow:
```
create_project → set_overview → upsert_milestone → open_period
→ upsert_month / create_task
→ transition_task in_progress (with branch) → ... → transition_task done
→ close_period (retrospective)
```
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.
## Self-hosting
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](https://github.com/Cafelatte1/project-auth),
a separate service. None of it is needed for the stdio install above.
[docs/self-hosting.md](docs/self-hosting.md) covers the setup;
[docs/operations.md](docs/operations.md) is the day-2 runbook.
## Development
```powershell
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.
More docs under [docs/](docs/INDEX.md):
- [docs/self-hosting.md](docs/self-hosting.md) — running FronyBoard as a shared server: clients, dashboard, hosted apps, deploy
- [docs/auth.md](docs/auth.md) — access channels (CLI agents, desktop, dashboard, hosted apps) and how each authenticates
- [docs/http-api.md](docs/http-api.md) — the FronyBoard JSON API
- [docs/data-model.md](docs/data-model.md) — field-level schema and validation rules
- [docs/operations.md](docs/operations.md) — home server runbook
What people ask about FronyBoard
What is Cafelatte1/FronyBoard?
+
Cafelatte1/FronyBoard is mcp servers for the Claude AI ecosystem. An MCP server that gives AI agents a first-class project tracker It has 0 GitHub stars and its last recorded update is dated 2026-09-08.
How do I install FronyBoard?
+
You can install FronyBoard by cloning the repository (https://github.com/Cafelatte1/FronyBoard) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is Cafelatte1/FronyBoard safe to use?
+
Our security agent has analyzed Cafelatte1/FronyBoard and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains Cafelatte1/FronyBoard?
+
Cafelatte1/FronyBoard is maintained by Cafelatte1. The last recorded GitHub activity is dated 2026-09-08, with 0 open issues.
Are there alternatives to FronyBoard?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy FronyBoard to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/cafelatte1-fronyboard)<a href="https://claudewave.com/repo/cafelatte1-fronyboard"><img src="https://claudewave.com/api/badge/cafelatte1-fronyboard" alt="Featured on ClaudeWave: Cafelatte1/FronyBoard" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
The fastest path to AI-powered full stack observability, even for lean teams.
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!