Click 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., "@Personal Context MCP Template load my full profile"
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.
Serve your personal "how to work with me" manual as an MCP server, so every AI surface you use (Claude Code, chat apps, scheduled agents) loads the same single source of truth about who you are and how you work.
This is a template: the profile sections here are placeholders with writing guidance. Fill them with your own content and you have your own personal-context server. It contains no real profile data and is deliberately independent from the author's actual profile.
Whoever works with multiple AI surfaces ends up explaining themselves over and over — and the explanations drift apart. The fix:
One canonical profile: identity, tracks, communication style, hard
rules, routines, interfaces, pointers. Markdown, one file per section, in
profile/.
A tiny MCP server that serves it: get_profile (everything),
get_section (one part), list_sections, plus each section as an MCP
resource.
Every surface starts by loading it. Interactive session or headless nightly run: same manual, same rules.
Changing an agreement is one edit in the canon, not a hunt across seven system prompts. Bonus: a periodic drift-check comparing all places where your context lives against this canon.
Two design rules that keep it healthy:
Identity only, no live data. Tasks, metrics and budgets live in the
systems that own them; the pointers section says where. Operational data
in a profile goes stale by lunchtime.
Identity first. The section order is deliberate: if an agent reads only
one file, it should be identity.md.
Related MCP server: MCP Employee Identity Server
Requires Python 3.10+.
pip install -r requirements.txt
# Local, stdio (simplest — Claude Code on the same machine):
claude mcp add my-profile --env MCP_TRANSPORT=stdio -- python -m app.server
# Or as an HTTP server (remote surfaces):
python -m app.server # http://0.0.0.0:8080/mcp, health at /healthThen fill in the seven files under profile/ — each explains what belongs in
it — and ask your agent to get_profile.
If you expose this over HTTP: put real access control in front (an authenticating proxy or tunnel). Your profile is personal by definition; the server itself does no auth.
Why this pattern exists and how it behaves in daily use: the write-up in werken-met-cos (Dutch). Built on Anthropic's Model Context Protocol.
MIT — see LICENSE.
This server cannot be deployed
Person-owned AI memory that learns, not just stores — portable context for any MCP client.
Governed personal world model and memory for your AI agent. Pair once, connect over MCP.
Hosted MCP server for AI agent identity, permissions, verification, and reusable proof.
One memory, every AI. A shared, user-owned markdown memory your AI clients read and write over MCP.
