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recent_nhi_amendments

Read-onlyIdempotent

List recent amendments to Taiwan NHI rules and audit guidelines. Access publication dates, titles, and direct links to official documents.

Instructions

List recent amendments to Taiwan NHI rules and audit guidelines (健保署「醫療費用審查注意事項」近一年修正公告). Returns the publication / effective dates, the amendment title and type ('primary' = main regulation update, 'comparison' = side-by-side diff document), and direct links to the official DOCX / ODT / PDF announcement files plus the 健保署 listing page. Use when an agent needs to answer 'what NHI rule changes are coming?', 'when does the new 審查注意事項 take effect?', or wants to check whether a specific rule has been recently amended before quoting it. Typical follow-up: call search_audit_guidelines({query}) to pull the post-amendment clause text, or lookup_rejection_code({code}) to see how it ties into existing rejection codes. Reference only — official 健保署 announcements are authoritative. Curated by OPDSTAR (https://opdstar.com).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
since_daysNoLimit to amendments effective within N days from today (1..3650). Default 365 (近一年).
typeNo'primary' = main regulation update (affects nhi_audit_clauses content); 'comparison' = side-by-side diff document. 'all' returns both. Default 'all'.all
limitNoMax results (1..50). Default 20.
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that the tool is for 'Reference only' and that official 健保署 announcements are authoritative, disclosing behavioral boundaries beyond annotations.

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?

Description is roughly 5 sentences, front-loaded with purpose and return details, followed by usage guidance, then a note on authority. Every sentence serves a distinct purpose without redundancy.

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

Completeness5/5

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

No output schema exists, but the description fully enumerates return fields (dates, title, type, links). Combined with clear parameter semantics and annotations, it provides complete context for agent decision-making.

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 has 100% coverage with parameter descriptions. The description adds value by clarifying the type enum values with parenthetical explanations (e.g., 'primary = main regulation update') and explicitly stating defaults in Chinese, but the schema already handles the basics well.

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?

Description explicitly states the tool lists recent amendments to Taiwan NHI rules and audit guidelines, specifying return fields (dates, title, type, links). It distinguishes from siblings by suggesting typical follow-ups like search_audit_guidelines and lookup_rejection_code.

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?

The description includes explicit 'Use when' scenarios (e.g., 'what NHI rule changes are coming?') and advises on alternatives via 'Typical follow-up' calls to sibling tools, providing clear contextual guidance.

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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