Skip to main content
Glama

user-db

Central user-state database as an MCP server: durable profile facts plus live virtual sensors (user stress level, room intensity) with configurable smoothing — readable in real time by any agent or smart-home service.

Data separation (important)

This repository contains software only. All personal data lives outside the repo, in the directory given by USER_DB_DIR (default ~/.config/user-db/):

File

Content

Written by

profile.json

durable user facts (name, speech id, birthday, profession, expertise, traits, ...)

you / agents via profile_set

config.json

sensor + curve configuration (reactiveness tuning)

you

state.json

live sensor state

this software

Nothing in this repo ever contains user data; examples/ holds neutral templates. Copy them to USER_DB_DIR to get started:

mkdir -p ~/.config/user-db
cp examples/*.json ~/.config/user-db/

Related MCP server: engram

Install & run

python3 -m venv .venv && .venv/bin/pip install mcp
claude mcp add --scope user user-db -- $PWD/.venv/bin/python $PWD/server.py

CLI (same core, for shell loops and Home Assistant command_line sensors):

bin/userdb profile
bin/userdb sensor report stress 70
bin/userdb sensor read stress
bin/userdb state

MCP tools

  • profile_get / profile_set(field, value) — the user's durable state.

  • sensor_read(name) / sensor_report(name, value) — virtual sensors.

  • state_get — all sensors + the curve combining both axes.

Virtual sensors & reactiveness

A sensor value is not a plain variable. Producers report raw observations (0–100); the stored value follows them via time-aware exponential smoothing:

alpha = 1 - exp(-dt / tau_seconds)
value += alpha * (raw - value)

tau_seconds per sensor in config.json is the reactiveness: small tau = reactive, large tau = inert. This keeps the sensor from jumping and lets you fine-tune it for smart-home automations. The first report initialises the value directly.

Default sensors: stress (the user's stress level, reported by a watchdog/perception agent) and room_intensity (the room's current scene intensity, reported by a scene-analysis agent — see docs/neuronal-interceptor.md).

The two axes & the curve

The system has two orthogonal axes: user stress and room intensity. A freely definable piecewise-linear curve in config.json maps stress to a target room intensity:

"curve": {"x": "stress", "y": "room_intensity",
          "points": [[0, 85], [40, 60], [70, 35], [100, 15]]}

state_get evaluates it and returns target and delta (actual − target) — the basis for smart-home decisions ("the user is stressed but the room is loud → calm it down").

Home Assistant

Expose the smoothed value as a command_line sensor, or bridge it through an existing MCP that already abstracts Home Assistant:

sensor:
  - platform: command_line
    name: user_stress
    command: "/path/to/user-db/bin/userdb sensor read stress | jq .value"
    scan_interval: 60

License

MIT

Install Server
A
license - permissive license
A
quality
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/NG-Bullseye/user-db'

If you have feedback or need assistance with the MCP directory API, please join our Discord server