MCP server for agentic project management — tasks, docs, decisions and time in one shared team context
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/frameonlab/frameon-mcp{
"mcpServers": {
"frameon-mcp": {
"command": "node",
"args": ["/path/to/frameon-mcp/dist/index.js"]
}
}
}Resumen de MCP Servers
# FrameOn MCP
**MCP server for agentic project management.** Tasks, documents, decisions and
time in one shared team context, reachable by an AI agent over the Model
Context Protocol.
This repository holds the **public surface** of that server: the protocol
types, the guide the server serves to connected clients, the skill playbooks,
and the published manifest. The server itself is hosted — you do not run it.
| | |
| --- | --- |
| **Endpoint** | `https://api.frameonlab.com/api/v1/mcp` |
| **Transport** | `streamable-http` |
| **Auth** | OAuth 2.1 — Dynamic Client Registration, PKCE (S256) required |
| **Tools** | 20 (13 read-only, 7 write) — see [docs/tools.md](docs/tools.md) |
| **Registry** | [`com.frameonlab/frameon`](https://registry.modelcontextprotocol.io/v0/servers?search=frameon) |
| **Docs** | https://app.frameonlab.com/mcp |
## Why this exists
An AI assistant asked to help with a project normally works from whatever got
pasted into the chat. It cannot see what was decided three weeks ago, which
constraint someone stated in a meeting, or who is already on the task. So it
guesses, confidently, and a human spends the afternoon correcting it.
FrameOn exposes the project itself: the task tree, the wiki where decisions
live, a separate project memory holding conventions and traps, the team, the
alerts and the time log. The agent reads the real state, writes back what it
did, and the next agent — on another machine, in another client — finds it
there.
## Connect
### Claude Code
```bash
claude mcp add --transport http frameon https://api.frameonlab.com/api/v1/mcp
```
### Claude Desktop, Cursor, and other `mcpServers` clients
```json
{
"mcpServers": {
"frameon": {
"type": "http",
"url": "https://api.frameonlab.com/api/v1/mcp"
}
}
}
```
### ChatGPT
Add a connector pointing at the same URL. The OAuth flow runs in the browser;
no key is pasted anywhere.
There is no API key in any of these. The first call opens an authorisation
screen, a human approves the workspace, and the client stores a token it
rotates on its own. A Personal Access Token also works, as
`Authorization: Bearer …`, for scripted use where no browser exists.
More, including a per-client walkthrough: https://app.frameonlab.com/mcp
## What the agent gets
Connect and call `frameon://guide`. The server hands back a written briefing —
what FrameOn is, which tool answers which question, the traps that cost a
round-trip, and, just as importantly, **what FrameOn has that the agent does
not**: the Gantt, the approval step on timesheets, the financial reports. A
tool that refuses to say where its edges are gets improvised around, badly.
## Scope of this repository
| Path | What it is |
| --- | --- |
| `src/mcp.types.ts` | JSON-RPC 2.0 envelope types and the protocol versions the server echoes |
| `src/mcp-guide.ts` | the text served at `frameon://guide`, plus the prompt specs |
| `src/mcp-skills.ts` | the skill playbooks behind `list_skills` / `get_skill` |
| `.mcp.json` | drop-in client config at the repo root — the Open Plugins entry point |
| `server.json` | the manifest published to the MCP registry |
| `docs/tools.md` | the 20 tools, with the descriptions the server advertises |
| `examples/` | client configuration, ready to paste |
The service implementation, the database and the tenant layer are not here and
are not open source. What is here is what a client talks to and what an agent
reads — enough to know exactly what you are connecting to before you connect.
## Security
The endpoint is multi-tenant and every query filters by tenant. A credential
reaches the projects its role reaches and nothing else; `tenant_id` never
appears in a tool response. Write tools are gated per call, on the scope and
the role carried by the credential — never on anything sent in the request
body.
Found something that looks wrong? `seguranca@frameonlab.com`. Please do not
open a public issue for a suspected vulnerability.
## Licence
MIT — see [LICENSE](LICENSE).
Lo que la gente pregunta sobre frameon-mcp
¿Qué es frameonlab/frameon-mcp?
+
frameonlab/frameon-mcp es mcp servers para el ecosistema de Claude AI. MCP server for agentic project management — tasks, docs, decisions and time in one shared team context Tiene 0 estrellas en GitHub y su última actualización registrada es del 2026-09-19.
¿Cómo se instala frameon-mcp?
+
Puedes instalar frameon-mcp clonando el repositorio (https://github.com/frameonlab/frameon-mcp) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar frameonlab/frameon-mcp?
+
Nuestro agente de seguridad ha analizado frameonlab/frameon-mcp y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene frameonlab/frameon-mcp?
+
frameonlab/frameon-mcp es mantenido por frameonlab. La última actividad registrada en GitHub es del 2026-09-19, con 0 issues abiertos.
¿Hay alternativas a frameon-mcp?
+
Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
Despliega frameon-mcp en tu cloud
Lleva este repo a producción en minutos. Cada plataforma genera su propio entorno con variables de entorno editables.
¿Mantienes este repo? Añade un badge a tu README
Pega el badge en tu README de GitHub para mostrar que está auditado por ClaudeWave. Cada badge enlaza de vuelta a esta página y muestra el Trust Score actual.
[](https://claudewave.com/repo/frameonlab-frameon-mcp)<a href="https://claudewave.com/repo/frameonlab-frameon-mcp"><img src="https://claudewave.com/api/badge/frameonlab-frameon-mcp" alt="Featured on ClaudeWave: frameonlab/frameon-mcp" width="320" height="64" /></a>Más 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
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ
The fastest path to AI-powered full stack observability, even for lean teams.