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Datailor Preference MCP
Give every AI agent your defaults
Datailor turns your recurring corrections into local, editable preferences that every agent can use. Local preference settings for any AI agent.
Datailor is a solution to a personal frustration.
Session memory is a dead end. Teach Claude your coding style today, start a new thread tomorrow, and you're explaining indentation preferences all over again.
AGENTS.md promises permanence, but it's a static wall of text that never learns, never adapts, and quietly becomes outdated the moment your workflow shifts.
LLM-native memory is opaque, unreliable, and hopelessly vendor-locked. Cross-device portability? Try migrating your "preferences" from Cursor to Claude to Codex and watch them evaporate.
I wanted something that actually understands the problem:
◼ Dynamic matching — knows when to surface a preference and when to stay out of the way
◼ Automatic capture — learns from what you do, not from what you claim you do
◼ Intentional expiration — lets stale preferences fade instead of accumulating digital hoard
◼ Conflict detection — surfaces contradictions instead of silently guessing wrong
So I built Datailor. One Markdown file. Any agent can read it.
Datailor is deliberately minimal, human-readable, and open source. I beleive AI tooling should be like good conversations: respectful, interactive, and actually listening.
About Datailor Preference MCP on Product Hunt
“Give every AI agent your defaults”
Datailor Preference MCP was submitted on Product Hunt and earned 11 upvotes and 4 comments, placing #77 on the daily leaderboard. Datailor turns your recurring corrections into local, editable preferences that every agent can use. Local preference settings for any AI agent.
On the analytics side, Datailor Preference MCP competes within Productivity, Open Source, Artificial Intelligence and GitHub — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Datailor Preference MCP performed against the three products that launched closest to it on the same day.
Who hunted Datailor Preference MCP?
Datailor Preference MCP was hunted by Rosetta (Zidian) Guo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Datailor Preference MCP including community comment highlights and product details, visit the product overview.
Rosetta (Zidian) Guo