FrameOn MCP
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@FrameOn MCPWhat tasks are due this week and who owns them?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 |
|
Transport |
|
Auth | OAuth 2.1 — Dynamic Client Registration, PKCE (S256) required |
Tools | 20 (13 read-only, 7 write) — see docs/tools.md |
Registry | |
Docs |
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.
Related MCP server: LogicMem MCP Server
Connect
Claude Code
claude mcp add --transport http frameon https://api.frameonlab.com/api/v1/mcpClaude 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 |
| JSON-RPC 2.0 envelope types and the protocol versions the server echoes |
| the text served at |
| the skill playbooks behind |
| drop-in client config at the repo root — the Open Plugins entry point |
| the manifest published to the MCP registry |
| the 20 tools, with the descriptions the server advertises |
| 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.
This server cannot be deployed
Maintenance
Related MCP Connectors
- OneLoreOAuthai.onelore
Shared project context for AI agents and teams: docs, tasks, and messages that stay current.
Shared project memory that keeps teammates and AI agents aligned across sessions.
Intelligent context infrastructure for AI teams: knowledge graph, sessions, tasks, documents.
One shared context your team's AI tools read & write over MCP. No re-explaining. Free.
Related MCP Servers
- AlicenseBqualityDmaintenanceProvides persistent memory, reasoning engine, agent-to-agent sharing, and immutable audit trail for AI agents via the Model Context Protocol.12MIT

LogicMem MCP Serverofficial
AlicenseBqualityDmaintenanceProvides persistent memory, reasoning, agent-to-agent sharing, and immutable audit trail for AI agents via the Model Context Protocol.121MIT- AlicenseNot gradedqualityDmaintenanceGives AI agents durable project memory via the Model Context Protocol, allowing them to read tasks, record decisions, search context, and sync snapshots to the cloud.4 npmMIT
- AlicenseNot gradedqualityCmaintenanceProvides persistent, project-specific memory to AI assistants via the Model Context Protocol, enabling context-aware collaboration across sessions without cloud dependencies.21 PyPI1Apache 2.0