FrameOn — Agentic Project Management
RemoteAbout FrameOn — Agentic Project Management
FrameOn is a hosted MCP server for AI agents working with shared project context, including tasks, documents, decisions, and time logs. Its tools and guide let agents read project state and write updates that remain available across clients and sessions.
Key features
- 01Access tasks, documents, and decisions
- 02Read project memory and team context
- 03Work with alerts and time logs
- 04Read a server-provided agent guide
- 05Use read-only and write tools
Use cases
- 01Check project decisions before proposing work
- 02Update tasks after an agent completes work
- 03Share project context across agents and clients
MCP support
FrameOn exposes a hosted MCP server over streamable HTTP, with OAuth 2.1 authentication. It provides 20 tools and the `frameon://guide` resource to connected MCP clients.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://api.frameonlab.com/api/v1/mcpclaude mcp add --transport http com-frameonlab-frameon https://api.frameonlab.com/api/v1/mcp{"mcpServers":{"com-frameonlab-frameon":{"type":"http","url":"https://api.frameonlab.com/api/v1/mcp"}}}Open in Cursor{"servers":{"com-frameonlab-frameon":{"type":"http","url":"https://api.frameonlab.com/api/v1/mcp"}}}Open in VS Code{"mcpServers":{"com-frameonlab-frameon":{"type":"http","url":"https://api.frameonlab.com/api/v1/mcp"}}}{"mcpServers":{"com-frameonlab-frameon":{"serverUrl":"https://api.frameonlab.com/api/v1/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEFrameOn 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 |
| Registry | com.frameonlab/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
claude mcp add --transport http frameon https://api.frameonlab.com/api/v1/mcp
Claude Desktop, Cursor, and other mcpServers clients
{
"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? [email protected]. Please do not
open a public issue for a suspected vulnerability.
Licence
MIT — see LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.