Example Workflow
Here is the simple idea:
- You connect
devmcp-context to your agent.
- The agent uses the MCP tools automatically when it needs to save, read, search, edit, or delete memory.
- The memory is stored in plain markdown files inside your project folder.
- If you want, you can still open those files and edit them manually yourself.
That gives you both automation and manual control.
Why This Is Useful
This setup is helpful when you want:
- Better continuity across sessions
- Less repetition in agent conversations
- Clear project memory you can audit
- A simple workflow that fits into Git-based projects
For me, the biggest win is that memory is no longer a black box.
Small Demo
In a real setup, the agent calls the MCP tools through the server, and you still keep full control of the files in the project folder.
context_save(category="decisions", key="auth-strategy", value="Use JWT with refresh tokens", tags=["security"])
context_load(category="decisions")
context_search(query="JWT")
Final Thoughts
devmcp-context is meant to be simple:
- file-based
- human-readable
- searchable
- editable
- persistent
That is what makes it useful: the agent can use the MCP tools automatically, but the memory still lives in plain files you can open, edit, or delete whenever you want.
If you are building AI-assisted workflows and want memory you can trust, this is a good place to start.
If you want to try it, check out the project docs and give it a spin.
Links