The MCP server with the slug ai-unlimitless-unl is described as: “Stop re-explaining yourself to your AI.” Beyond the provided name and description, no additional server capabilities, tools, topics, or readme excerpt details are included in the source data.
🛠️ Key Features
Server name: ai.unlimitless/unl
Description: “Stop re-explaining yourself to your AI.”
🚀 Use Cases
Reduce repetition when interacting with an AI (as implied by the provided description).
⚡ Developer Benefits
Clear, concise purpose statement via the server description.
Known identifier/slug: ai-unlimitless-unl.
⚠️ Limitations
No tool count, topics, or README excerpt provided.
Functionality beyond the one-line description is not specified in the available data.
Unl, your why agent. Your AI already knows how to reason. Unl gives it your reason.
See it work in two minutes, no account: node demo/changed-decision.mjs
You decide things while you work with AI: what to build, what to leave out, and why. Unl keeps those decisions with their reasons. When you or one of your agents is about to act, Unl hands over the ones that bear on the task, so Claude Code, Cursor, ChatGPT, Codex and the agents you build all start from the same why. You never re-brief them.
What is open, and what is hosted
Open, in this repository, under MIT: everything that runs on your side. The unl command, the AI SDK package, the Cursor plugin, a working example for each agent framework, and the decision format with a conformance check you can run on your own code. Read them to see exactly what leaves your machine.
Also open: the status checks, which test Unl from outside its own infrastructure.
Hosted: Unl itself, the service that keeps your decisions with their reasons and chooses which ones bear on a task, runs at api.unlimitless.ai. Its code is not in this repository.
Start
With the agent you already use, tell it:
text
Read unlimitless.ai/start.md, set up Unl, and show me the decisions you find.
In Claude Code or Cursor:
bash
npm install -g unlimitless
unl # sign in once, then the Unl terminal opens
unl init # in your project, for Claude Code (add --cursor for Cursor)
Then ask your agent what you are working on.
In any MCP client (ChatGPT, Claude, Codex, VS Code, Grok Bot, Lovable): add the server https://api.unlimitless.ai/mcp and sign in when it asks.
In an agent you build: make a key at unlimitless.ai/portal/keys, set it as UNL_KEY, and pick your framework below.
Connect your tools
Every tool uses the same address, https://api.unlimitless.ai/mcp, and signs in with OAuth once. There is no key to paste: a personal key is only for the API, never for connecting a tool.
Claude and Claude Desktop. Open Settings, then Connectors, and choose Add custom connector. Name it Unl, paste https://api.unlimitless.ai/mcp, and add it.
Claude Code. In your project, unl init signs you in and wires the hook that brings Unl into every turn. To add only the server:
bash
claude mcp add --transport http unl https://api.unlimitless.ai/mcp
then run /mcp, choose unl and pick Authenticate.
ChatGPT. In Settings, open Plugins and switch on Developer mode. Choose New Plugin, name it Unl, paste https://api.unlimitless.ai/mcp as the server URL, set authentication to OAuth and keep Dynamic Client Registration. Then open Advanced OAuth settings and untick OIDC enabled: left on, ChatGPT asks for a scope Unl does not register and the connection is refused.
Cursor. Add the URL to .cursor/mcp.json, no key and no header, or install the Cursor plugin in this repository:
The unl command. unl init adds Unl to a Claude Code or Cursor project, and unl on its own opens the Unl terminal: each turn, the decisions Unl handed your agent with their reasons, and the ones it held back.
One small working example per framework: Mastra, OpenAI Agents SDK, Google ADK, LangChain, Pydantic AI, LiteLLM, Cloudflare Agents, Netlify, TanStack AI, Composio, E2B, Browser Use and a GitHub Copilot custom agent.
The open format for a kept decision: what it holds, how its address is computed and checked, a conformance check you can run on your own implementation, and reference code in Node and Python. MIT, and it runs without Unl.
Try the client contract locally
bash
git clone https://github.com/Unlimitless-Inc/unl.git
cd unl/packages/ai-sdk
npm install
npm test
These tests use synthetic responses. They test the client contract without an account or a model call, and do not prove hosted retrieval or model performance. The planner and writer example is the next step for trying a real changed decision in your own test project.
For example, you first choose synchronous exports because the files are small. Later you choose a queue because larger files exceed the request timeout. The job is for a fresh writer to receive the new choice and its reason, then produce a change that follows it. Compare with an equally updated file as well as stale or absent context. A maintained file may work just as well.
How an agent gets your why
Two ways in, and most examples show both:
On every call. A middleware, hook or instructions function asks Unl what bears on the turn and puts the answer in front of the model before it acts.
Through Unl's tools over MCP. The agent can open the full reasoning behind a decision, and propose a new one for you to confirm. Nothing becomes one of your decisions until you say so.
Follow the build
Unl ships changes every day, and the changelog says in plain words what each one did. To follow along, star this repository.
Contributing and security
Bug reports and small fixes are welcome: see CONTRIBUTING.md. To report a security problem, follow SECURITY.md and do not open a public issue.
