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RegAI Legal MCP (Taiwan)

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search_decisions_exact

Read-onlyIdempotent

List EVERY Taiwan (ROC) court decision in the apex-tier corpus whose text contains the given literal phrase(s) -- with the exact total count, newest first, paged. Use it when the user wants all decisions (or how many) containing a term (「列出所有…」「有幾筆…」), quotes a phrase to find, or asks whether a term appears at all; optionally narrowed by court, case type and decision-date years, with NOT-phrases. Matching is literal (whitespace ignored), never by meaning: for a legal concept, fact pattern, or anything phrased in your own words, use search_decisions instead, which ranks by relevance. Each row gives the citation, date, 案由, match count, jid, and the first matching passage; use get_decision_details(jid) for full text. The first page already gives the exact total: to answer 'how many', do not page. Request further pages only when the user asks to see more rows. Same corpus as search_decisions (top courts only -- no district or high courts).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo1-based page number (default 1). Results are newest first.
courtNoOptional court filter, exactly one of: 最高法院, 最高行政法院, 憲法法庭, 懲戒法院, 懲戒法院懲戒法庭, 懲戒法院職務法庭, 智慧財產及商業法院, 大法庭 (Grand Chamber rulings).
termsYesLiteral phrases in Traditional Chinese that must ALL appear in a decision's text (AND), e.g. ["契約承擔"] or ["借名登記", "信託"]. Each 2-100 characters; matched exactly as written (whitespace ignored), never by meaning -- a synonym or paraphrase will not match. Do not put the court name here; use `court`.
excludeNoOptional literal phrases that must NOT appear in the decision, e.g. ["租賃"]. terms + exclude: at most 6 in total.
year_toNoOptional: latest decision-date year, inclusive; same format as year_from.
page_sizeNoDecisions per page (default 20, max 50).
year_fromNoOptional: earliest decision-date year, inclusive. ROC (民國) year such as 109, or Gregorian such as 2020 (values up to 200 are read as ROC). Filters on the date the decision was issued (裁判日期), not the year in the case number.
case_typesNoOptional case-type filter: any of C (憲法), V (民事), M (刑事), A (行政), P (懲戒). Omit for no restriction.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses literal (whitespace-insensitive) matching semantics, the corpus boundary (top courts only, no district/high courts), the total-count-on-first-page behavior, and the shape of each returned row. These are non-obvious behavioral traits an agent cannot infer from annotations.

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?

One dense paragraph, front-loaded with the core scope before routing guidance. Every clause carries information, though the parenthetical example queries make it longer than strictly necessary.

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?

With no output schema, the description covers return values (citation, date, 案由, match count, jid, first passage), points to get_decision_details for full text, and explains count-vs-paging behavior. Nothing needed to call it correctly is missing.

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 100%, so the baseline is 3; the description still adds value by reinforcing that `terms` are literal AND-matched phrases (synonyms will not match) and that the first page carries the exact total, which governs whether page/page_size are used at all.

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 specific verb (list) and resource (every Taiwan ROC apex-tier court decision) plus scope constraints (literal phrase match, exact count, newest first, paged). It explicitly distinguishes itself from search_decisions by contrast ('never by meaning... use search_decisions instead').

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?

Gives explicit triggering conditions ('列出所有…', '有幾筆…', quoting a phrase, asking whether a term appears) and an explicit exclusion with the named alternative for conceptual/fact-pattern queries. Also instructs not to page when only the count is wanted.

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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