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masoudroot
by masoudroot

Mechanical Pass

mechanical_pass

Fix Persian text mechanical errors like Arabic characters, punctuation, and spacing, then verify no issues remain. Returns remaining problems and a clean flag for use as a first or final editing step.

Instructions

Deterministic mechanical fix + verification gate for Persian text.

Fixes what needs no judgment (Arabic ي/ك, Latin , ; ? %, straight quotes, ZWNJ on می/نمی, spacing) and proves the result: remaining lists any mechanical issue left, clean is true only when none remain. Use it as the first step (so the LLM works on a clean base) and as the final gate (so nothing ships with mechanical errors). Ambiguous cases (em/en dashes) are flagged in remaining, never guessed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden, and it does most of the work: deterministic non-judgmental behavior, ambiguity flagged rather than guessed, and the meaning of the output fields `remaining` and `clean`. It does not state whether the corrected text is returned (vs. mutated in place), idempotency, or failure behavior on empty/non-Persian input, which keeps it short of a 5.

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

Conciseness5/5

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

Front-loads the purpose in one line, then the verification contract, then usage. The parenthetical list of fix categories is dense but each item is concrete and earns its space; no filler sentences.

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 return values need not be spelled out, yet the description still explains the semantics of `remaining` and `clean`, which is the right kind of added value. The only mild gap is not saying explicitly that the corrected text is part of the result, but the output schema covers that.

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

Parameters3/5

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

One parameter (`text`) with 0% schema description coverage, so the description must compensate. Reading the prose makes it obvious the input is the Persian text to normalize, but no format, length, or encoding expectations are stated. Baseline 3 for a minimal single-param tool.

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

Purpose5/5

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

States a precise verb+resource: a deterministic mechanical fix plus verification gate for Persian text. It also draws its own boundary ('fixes what needs no judgment', 'never guessed'), which lets an agent distinguish it from judgment-based siblings such as ruling or rule_pack without opening a schema.

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

Usage Guidelines5/5

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

Explicitly prescribes placement: 'Use it as the first step (so the LLM works on a clean base) and as the final gate (so nothing ships with mechanical errors).' That is both when-to-use and the workflow position, with the rationale for each.

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