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ZTL Judge

by io.github.inventor1975
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judge_naive

Judge sets defined by a property, self-reference allowed (Russell's set, a web of trust, a policy rule).

Args:
    universe: {"sets": {name: {"list": [...]} | {"data": {name: "T"|"F"|"Z"}} | {"abs": formula}},
        "claims": [formula, ...]}. Formulas: ["in", a, S], ["not", f], ["and"|"or"|"imp"|"iff", f, g],
        ["all"|"ex", "y", f], ["eq", S, T]; an abs set's own variable is "x". Every name is also an object.

Returns:
    Per set: the members and non-members the definitions ground, and a passport for each membership they
    leave undecided — PARADOX (no stipulation can repair it), UNDERDETERMINED (consistent; a decision is
    owed), INTRINSIC, INPUT (verify the given cell), DOWNSTREAM (inherits a quarantine). Per claim: EARNED /
    REFUTED / EARNED IF STABLE / REFUTED IF STABLE / OPEN, the cells it rests on with their passports, and for
    a universal claim the instances left undecided. Oversized requests are refused with the reason.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "universe": "<universe>"
    },
    "name": "judge_naive"
  }
}

Input Schema

NameRequiredDescriptionDefault
universeYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full burden and does substantial work: it discloses that requests can be refused for size, that verdicts split into EARNED/REFUTED/OPEN and stability-conditional variants, and that undecided memberships receive passports. It stops short of characterizing cost or how the naive engine differs behaviorally from its sibling.

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?

The first sentence front-loads purpose, and the Args/Returns layout is scannable despite the dense domain vocabulary. Some of the Returns section duplicates classification detail likely available in the output schema, but most sentences earn their place.

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

Completeness3/5

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

For a high-complexity logical evaluator, the description covers the input DSL and output vocabulary well, and an output schema exists so return values need not be re-explained. What is missing is the relationship to the sibling 'judge' tool and any explanation of the string variant of the universe parameter, which leaves a real decision under-informed.

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 an untyped anyOf of object-or-string with additionalProperties true, so the description must compensate, and it largely does: it specifies the universe dict shape, the three set forms (list/data/abs), the formula grammar, and that an abs set's own variable is 'x'. Gaps remain — the string form of the parameter is never explained, and operator arity/meaning for 'eq' and quantifiers is only partially spelled out.

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 opens with a concrete verb plus resource ('Judge sets defined by a property') and immediately flags the key capability (self-reference, with Russell's set and web-of-trust as illustrations). It is clear what the tool does, but it never distinguishes itself from the sibling tool named 'judge', leaving the 'naive' qualifier unexplained.

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?

Usage is only implied through the domain example (self-referential sets). There is no statement of when to prefer this over the sibling 'judge', no prerequisites, and no guidance on input sizing beyond a vague note that oversized requests are refused. An agent must infer the selection rule itself.

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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