Project management for AI agents: tasks, docs, decisions and time in one shared team context.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
One-click editor setup isnβt available for this listing yet β we donβt have a confirmed install command, and weβd rather show nothing than point your editor at the wrong package or host. Follow the projectβs own setup instructions, linked above.
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 β you do not run it.
| Endpoint | https://api.frameonlab.com/api/v1/mcp |
| Transport | streamable-http |
| Auth | OAuth 2.1 β Dynamic Client Registration, PKCE (S256) required |
| Tools | 20 (13 read-only, 7 write) β see docs/tools.md |
| Registry | com.frameonlab/frameon |
| Docs | https://app.frameonlab.com/mcp |
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 β on another machine, in another client β finds it there.
mcpServers clientsAdd 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 β¦, for scripted use where no browser exists.
More, including a per-client walkthrough: https://app.frameonlab.com/mcp
Connect and call frameon://guide. The server hands back a written briefing β
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.
| 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 β 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 β enough to know exactly what you are connecting to before you connect.
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 β 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.
MIT β see LICENSE.
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