ZTL Judge

by io.github.inventor1975

judge

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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`document`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 29, 2026

TDQS

A3.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden, and it does substantial work: it explains exactly how to interpret verdicts (T EARNED vs F REFUTED vs F/Z OPEN as NOT ESTABLISHED, ON CREDIT as holding on an unverified ground), warns against misreporting OPEN as false, and states the report is returned regardless of `ask`. It does not state side effects, idempotency, or whether any state is mutated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Content is front-loaded (purpose first, then Args, then Returns) and the verdict-interpretation sentences each carry real information. It is dense but not padded, though the run-on dash-heavy quoting in the middle paragraph makes the disposition rules harder to scan than they need to be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the description is not obliged to enumerate return values, yet it usefully summarizes what the verdict contains (disposition, grade, receipt, instruments, issues, what the core read). Given the domain's complexity, the main gap is the absence of any sibling routing or ZFL vocabulary grounding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the schema itself is only an untyped anyOf object|string, so the description must compensate — and it partially does by spelling out the document shape: rows of {name, means, status, ground} plus a claim, and noting the document may be passed as an object or JSON text. It stops short of defining what each row field means, so it is strong but not complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource — judge a ZFL document under ZTL — and characterizes the logic engine (three values, two-valued connectives). It is clear what the tool does, though it does not explicitly contrast itself with the siblings 'examples' and 'language', and a reader with no ZFL background gets only a hint of what 'judging' means.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use statement, and no mention of the sibling tools that would tell an agent whether to reach for judge versus examples or language. Usage is implied by the argument description alone, which is the minimum-viable level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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