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masoudroot
by masoudroot

Rule Pack

rule_pack

Builds a compact checklist of verified Persian writing rules for a specified task type, returning up to max_rules items for editing.

Instructions

Build the compact «بستهٔ قاعده» checklist for a Persian writing task.

task_type is one of: گزارش رسمی، ایمیل اداری، لندینگ، مقاله، کپشن، نامهٔ اداری، پروپوزال، خبر، مصاحبه، متن وب، پست شبکهٔ اجتماعی، جواب چت. (Any other value is used as a free-form search query.) Runs seed queries through the vault, keeps only status=verified notes, and returns up to max_rules items as «عنوان: حکم کوتاه». This is step 2-3 of the «حالت کامل» pipeline in the Persian OS.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_rulesNo
task_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does so reasonably: it discloses the internal process (runs seed queries through the vault), a filtering rule (keeps only status=verified notes), and the output shape («عنوان: حکم کوتاه»). It omits any explicit read-only statement, rate limits, or behavior when no verified notes match, but for a read-lookup tool this is solid disclosure.

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?

Purpose is front-loaded in the first line, then mechanics and pipeline placement follow in distinct blocks; the long enum list is necessary because the schema lacks it. No filler sentences, though the pipeline reference adds little for an agent that has no pipeline context.

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?

For a two-param tool with an output schema and no annotations, the definition covers purpose, input values, filtering logic, and result format adequately. The main gap is the absence of any routing guidance toward the several closely-named rule siblings, which the agent would otherwise have to guess between.

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 description coverage is 0% and the schema declares no enum, so the description's enumerated task_type list is essential and fully compensates; it also explains the fallback for unrecognized values (free-form search query). max_rules is clarified via 'returns up to max_rules items', though its integer/default nature is left to the schema.

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?

States a specific verb+resource ('Build the compact checklist for a Persian writing task') and pins its place in a pipeline, so the agent knows it produces a checklist rather than a search result. It does not explicitly distinguish itself from rule-flavored siblings like deep_rules or smart_rules; 'compact' hints at the distinction but never names the alternatives.

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?

It gives real context ('step 2-3 of the «حالت کامل» pipeline') and enumerates valid task_type values, which implies when to reach for it. However it never states when NOT to use it, nor points to deep_rules/smart_rules/ruling as alternatives for a different depth or purpose.

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