Liminality

ai.physeav1.0.5更新於 Oct 1, 2026

Breaks a hard question or decision into checkable sub-questions, grounds each to a real tool.

已驗證Streamable HTTP可網頁執行Other

概覽

AI 產生的概覽

一個託管推理服務,將難題或決策拆解為子問題,把每個子問題對應到真實工具,並回傳可查核的結果。

功能
Liminality 扮演助手與其可呼叫工具之間的推理層。其 solve 工具接收非平凡的問題、決策或多步驟任務,將其拆解為會影響結果的子問題,把每個子問題對應到真實工具或端點,並回傳帶評分的決策框架或有所依據的答案。research 工具執行更深入的多來源檢索,ask_form 與 apply_form 收集使用者的具體資訊,get_my_context、register_asset、set_preference、report_feedback、report_outcome 與 composio_connect 則用於管理脈絡、偏好、回饋與工具連線。
適用情境
適合用於一次性猜測容易出錯的困難、高風險問題或決策,而非快速查詢。當你需要結構化、可重現且能交給他人得到相同結果的路徑,或答案取決於需要簡短表單收集的使用者具體資訊時,值得加入。
執行需求
透過 streamable HTTP 存取的遠端託管伺服器,無需在本機建置或執行。需要 API 金鑰,透過 X-API-Key 標頭(或 Authorization: Bearer)傳送,或使用 OAuth 2.1 登入。可在註冊頁取得免費金鑰,內含 50 次免費求解。需要能連線至該端點的網路。
安裝前請注意
X-API-Key 標頭是必要的密鑰,請勿放入共享設定檔。已求解的請求會以可重複使用的路徑保存在共享資料庫中,服務也會保留關於你及你所連接內容的脈絡,請考慮要傳送哪些資訊。composio_connect 可連接外部工具以對其執行動作,report_feedback 與 report_outcome 會把使用資料回傳給服務。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Liminality,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "liminality": {
      "type": "http",
      "url": "https://liminality.physea.ai/mcp"
    }
  }
}

README

[Liminality]

Liminality

An MCP server that breaks a hard question or decision into the sub-questions that actually decide it, ties each to a real tool, and hands back a worked answer you can check.

Docs · Get a free key · Examples

[Liminality on Glama] [Official MCP Registry]


Give Liminality a tough question, a real decision, or a multi-step task. It decomposes the request into the sub-questions that change the outcome, grounds each one in a real tool or endpoint, and returns a result you can verify: a scored decision frame for a choice, a grounded answer for a question. It earns its keep on the work one-shot guessing gets wrong, the hard and high-stakes stuff, not quick lookups.

Every solved ask is saved as a reusable route in a shared library, so the next close question does not start from zero. It runs as a hosted remote server over streamable HTTP. New accounts and keys include 50 free solves.

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

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

Cursor

Add to ~/.cursor/mcp.json:

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

VS Code

Add to .vscode/mcp.json:

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


© 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.

來源:README.md,提交 de5e558

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  1. v1.0.5最新Sep 16, 2026