Liminal

Augment your work with Liminal. Stop repeating yourself and build on what’s already solved.

Docs · Get a free key · Examples

Liminal turns objectives into structured work by strategizing through an atomic, deterministic basis: setting guardrails around scope, compiling resources and constraints, uncovering gaps, and organizing research and next steps. All while carrying continuity and priorities forward so your AI builds on what’s already done.

Connect easily through MCP or use the desktop app.

Install

Remote server, nothing to build or run locally. Get a key at https://physea.ai/signup, then add it to your client. Ready-to-copy files for each client are in examples/, with a first solve walkthrough.

Claude Code

claude mcp add liminality --transport http https://liminality.physea.ai/mcp --header "X-API-Key: YOUR_KEY"

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "liminality": {
      "url": "https://liminality.physea.ai/mcp",
      "headers": { "X-API-Key": "YOUR_KEY" }
    }
  }
}

VS Code

Add to .vscode/mcp.json:

{
  "servers": {
    "liminality": {
      "type": "http",
      "url": "https://liminality.physea.ai/mcp",
      "headers": { "X-API-Key": "YOUR_KEY" }
    }
  }
}

• Endpoint: https://liminality.physea.ai/mcp
• Transport: streamable HTTP (remote, hosted)
• Auth: API key via X-API-Key or Authorization: Bearer, or OAuth 2.1.
• Registry name: ai.physea/liminality (Official MCP Registry)

What it is

Liminality is a reasoning layer that sits between your agent and the tools it could call. Instead of answering from memory, it works out the structure of the request first, then routes each piece to something real.

It runs on context-dependent determinism. Same question with the same known context routes the same way every time, so a result is reproducible and you can hand the route to someone else and get the same thing back. Change the context, and the route adapts to it. The determinism is in how it decides, not a frozen cache of canned answers.

Tools

• solve is the front door. Give it anything non-trivial. It works out the structure (decompose, ground to real tools, score the decision) and returns a worked result: a scored decision frame for a choice, or a grounded answer for a question.
• research runs a deeper multi-source pass that pulls real information for a question.
• ask_form / apply_form: when the answer depends on your specifics, it hands back a short multiple-choice form. Relay it, then apply_form folds the answers into a sharper result.
• get_my_context: what's known about you and what you've connected.
• register_asset / set_preference: tell it about your material and how you like results.
• report_feedback / report_outcome: tell it how a result did, so routes improve with use.
• composio_connect: connect a tool so an action can run against it.

Discovery

• https://liminality.physea.ai/.well-known/mcp
• https://liminality.physea.ai/.well-known/agent.json
• https://liminality.physea.ai/llms.txt

Links

• Site and docs: https://physea.ai/mcp
• Get a key: https://physea.ai/signup
• Privacy: https://physea.ai/privacy-policy
• By Physea

© 2026 Physea. All rights reserved. "Liminality", the name, and the logo are trademarks of Physea. This repository is a listing for a hosted service; no rights to the service, its software, or its data are granted. Use of the service is governed by the terms at https://physea.ai/mcp.