What is the “me” context?
The “me” context is a special default context always available. It contains global rules, snippets, and summaries that apply to every conversation. When loading context, the “me” context is always included automatically.
@phlax
Basic information
MCP server to persist chat artefacts and rules
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"remember-me": {
"command": "python",
"args": [
"-m",
"mcp.server.run",
"remember_me_mcp_server.server"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Remember Me is a persistence framework for maintaining conversational context and rules in MCP-based language model applications. It stores, retrieves, and manages persistent resources like rules, snippets, and summaries using SQLite, and is designed for developers building LLM applications that need durable context across conversations.
Install the package with pip install -e ., then run the MCP server with python -m mcp.server.run remember_me_mcp_server.server. Configure your LLM platform to connect to the MCP server and expose the tools. In conversations, the LLM should first load the context and then follow any rules returned.
The “me” context is a special default context always available. It contains global rules, snippets, and summaries that apply to every conversation. When loading context, the “me” context is always included automatically.
You can load additional contexts by specifying them in the `extra_context` parameter. This allows organizing different sets of resources for different conversation types, such as a “coding” context with programming snippets.
Remember Me supports three resource types: Rules (behavior constraints), Snippets (reusable code or text fragments), and Summaries (important contextual information).
You can create a backup of the current state with `my_context_backup_create()`, list available backups with `my_context_backup_list()`, restore from a backup with `my_context_backup_restore()`, and remove backups individually or clear all with `my_context_backup_remove()` and `my_context_backup_clear()`.
The server runs on Python and uses SQLite for persistence. It communicates via the MCP protocol and requires an MCP‑compatible LLM platform to expose its tools.

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