ZTL Judge
Zero-trust logic judge: your AI writes a claim as a ZFL table, the ZTL core judges it.
我該用這個嗎
品質與安全性
發現項目(1)
- LOW在 language 中
根據工具定義與協定合規性的自動化分析。
上下文成本
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案:
{
"mcpServers": {
"ztl-judge": {
"url": "https://api.vitalyreznik.com/mcp"
}
}
}遠端端點
https://api.vitalyreznik.com/mcpstreamable-http它能做什麼
工具清單
工具(3)
⚪language
The ZFL language: the columns of a row, the document fields, their meaning and rules.
輸入結構描述
{
"type": "object",
"properties": {},
"title": "languageArguments"
}輸出結構描述
{
"type": "object",
"additionalProperties": true,
"title": "languageDictOutput"
}⚪examples
Worked examples: questions already written as ZFL documents, ready to judge.
輸入結構描述
{
"type": "object",
"properties": {},
"title": "examplesArguments"
}輸出結構描述
{
"type": "object",
"additionalProperties": true,
"title": "examplesDictOutput"
}🟢judge(document)
Judge a ZFL document with ZTL, a zero-trust logic: three values, two-valued connectives. Args: document: The ZFL document, as an object or as JSON text: {"rows": [{"name": ..., "means": ..., "status": ..., "ground": ...}], "claim": ...}. Returns: The verdict with its disposition and grade, the receipt, the instruments that applied, the issues found, and what the core read. Read the verdict WITH its disposition: T EARNED = established; F REFUTED = false; F OPEN or Z OPEN = NOT ESTABLISHED, it could still turn either way (do not report it as false) — `why` and `unverified` say what to check; ON CREDIT = holds only on an unverified ground. A compound claim gets T or F; a claim that is a single name gets that name's own value, Z while unverified. The full report is returned whatever `ask` says.
輸入結構描述
{
"type": "object",
"properties": {
"document": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "string"
}
],
"title": "Document"
}
},
"required": [
"document"
],
"title": "judgeArguments"
}輸出結構描述
{
"type": "object",
"additionalProperties": true,
"title": "judgeDictOutput"
}社群
證據