Taiwan law and court decisions for your AI: statutes, articles and rulings, read-only.
Scored across 9 tools
Each tool targets a clearly distinct retrieval need, and descriptions explicitly guide when to use one over another (e.g., search_decisions vs search_decisions_exact vs search_grand_chamber_decisions). The law-side tools are also distinct: article-level, whole-law, hierarchy, title resolution, and conceptual search. No meaningful overlap remains.
All tool names use consistent snake_case with a clear verb-first pattern (get_* and search_*). The noun phrases are specific and parallel enough (get_article_by_number, get_decision_details, get_law_details). No mixing of conventions or vague verbs.
Nine tools is well-scoped for a legal retrieval server covering both statutes and decisions. Each tool earns its place by addressing a specific retrieval pattern. There is no bloat or thinness.
The surface covers search, exact-count search, grand-chamber precedent search, title resolution, article retrieval, full-law retrieval, hierarchy retrieval, and decision detail retrieval. A minor gap is the lack of direct citation-based lookup for decisions without first searching, but core research workflows are well supported.
Fetch the exact text of one Taiwan law article by law name + article number. Use this instead of search_law when you already know which article you want. Returns the current version (and, for a pending amendment, its historical counterpart too).
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Optional. "en" returns the official government English translation of the article when one exists (only ~21% of laws have one); omit or "zh-TW" for the Traditional Chinese text. | |
| law_name | Yes | Exact law name in Traditional Chinese, e.g. "民法", "中華民國刑法", "勞動基準法". Use the official short name; search_law_titles can resolve a partial name. | |
| article_number | Yes | Article number as it appears in the law, e.g. "184", "12-1", "1211-1". Do not include the 條 character. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing the return semantics: the current version, plus the historical counterpart when an amendment is pending. It doesn't cover rate limits or failure modes on unknown article numbers, keeping it from 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tight sentences with zero filler: purpose, routing guidance, and return behavior, in that order. Every sentence carries distinct information and the critical routing hint is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description proactively explains what comes back (current version, plus historical counterpart for pending amendments), which is the main unknown for this tool. Nothing essential is missing, though edge behavior for unknown laws or missing articles is unaddressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so law_name, article_number, and lang are fully documented in the schema (including the ~21% English-translation caveat and the 條 exclusion). The description only restates 'law name + article number' without adding syntax or format detail, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Fetch the exact text of one Taiwan law article') scoped by law name plus article number, which is a precise retrieval operation. It is clearly distinguishable from search_law and get_law_details by the 'one article' scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly routes the agent: 'Use this instead of search_law when you already know which article you want.' The condition that selects this tool over the sibling is stated outright, and the schema further points to search_law_titles for resolving partial names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full text of one Taiwan court decision by its jid (as returned by search_decisions). Returns every fragment of that ruling in order -- use it to read a decision in full after search_decisions surfaces it.
| Name | Required | Description | Default |
|---|---|---|---|
| jid | Yes | The decision's identifier (jid) exactly as returned by search_decisions, e.g. "TPSM,110,台上大,3997,20220428,2". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavior: it returns every fragment of the ruling in order, which tells the agent the result is a complete, ordered document rather than a snippet or summary. It doesn't cover failure behavior for an invalid jid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: the operation and its return shape come first, the workflow relation to search_decisions second. No filler, no repetition of the annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description usefully characterizes the return value as the full ordered set of fragments. For a single-parameter, read-only lookup it is nearly complete; it omits only edge behavior such as what happens when the jid is unknown or the decision is unavailable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is a single parameter with 100% schema description coverage, including a concrete jid example, so the schema already does the heavy lifting. The description reinforces provenance ('as returned by search_decisions'), which is useful but duplicates the schema text rather than adding syntax or format detail. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (fetch) and resource (full text of one Taiwan court decision) keyed by jid, and explicitly separates it from the sibling search tools by naming search_decisions as the discovery step. An agent can distinguish it from get_law_details and get_article_by_number without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives clear sequencing guidance: use it to read a decision in full *after* search_decisions surfaces it. That is a concrete when-to-use condition. It does not mention when not to use it (e.g., batch retrieval or exact-match lookups via search_decisions_exact), so it stops short of full alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the full text of an entire Taiwan law (all articles, with chapter/section headings), formatted as Markdown. For a single article prefer get_article_by_number; use this when you need the whole statute. The law counterpart of get_decision_details.
| Name | Required | Description | Default |
|---|---|---|---|
| law_name | Yes | Exact law name in Traditional Chinese, e.g. "公司法". Use search_law_titles first if you only have a partial or informal name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful context the annotations do not: the returned payload is Markdown including chapter/section headings. It does not mention auth or any truncation/size limits for very large statutes, which is the only real gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tight sentences, no filler: capability and return format first, then the disambiguation rule, then a one-line sibling analogy. Every sentence earns its place and the most important routing info is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read tool with no output schema, the description supplies the missing return-shape information (full statute as Markdown with headings) and full sibling routing, while annotations carry the safety profile. An agent has everything needed to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single law_name parameter is fully documented in the schema, including the Traditional Chinese requirement and the search_law_titles fallback. The description adds no parameter detail beyond that, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a precise verb and resource (return full text of an entire Taiwan law) plus scope (all articles, chapter/section headings) and output format (Markdown). It differentiates itself from siblings by naming get_article_by_number and get_decision_details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit routing: 'For a single article prefer get_article_by_number; use this when you need the whole statute.' It names the alternative and the condition that selects it, leaving nothing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the chapter/section/article outline of one Taiwan law. Useful for orienting in a large statute before drilling into specific articles with get_article_by_number.
| Name | Required | Description | Default |
|---|---|---|---|
| law_name | Yes | Exact law name in Traditional Chinese, e.g. "公司法". Use search_law_titles first if you only have a partial or informal name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description's added value is the returned structure (chapter/section/article outline). That is genuinely useful given no output schema exists, though it says nothing about size limits or depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two front-loaded sentences with no filler: the first states the output, the second states the use case and the follow-up tool. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter read tool with full annotation coverage, the description supplies what annotations cannot: the shape of the result. Minor gaps around output size or nesting depth remain, but an agent has enough to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single parameter is fully documented in the schema, including the Traditional Chinese format and the search_law_titles fallback. The description adds nothing beyond the schema here, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Return the chapter/section/article outline of one Taiwan law'), which is exactly distinguishable from siblings like get_law_details and get_article_by_number. The scope ('one Taiwan law') is explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage context ('orienting in a large statute') and names the alternative workflow, get_article_by_number, for drilling down. It lacks an explicit when-not-to-use case, but the routing to a sibling is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Taiwan (ROC) court decisions by legal concept, fact pattern, or keyword, ranked by relevance (hybrid semantic + keyword search, reranked). Covers an apex-tier subset: the Supreme Court, Supreme Administrative Court, Constitutional Court, Disciplinary Court and Intellectual Property and Commercial Court (district and high court decisions are not included). Use it for questions like "how have courts ruled on X" or "find rulings on this fact pattern"; it returns the most relevant decisions, not every match. Use search_decisions_exact instead when the user needs EVERY decision containing a literal phrase, or an exact count; search_grand_chamber_decisions when the question is which interpretation prevails on a point where panels diverged (大法庭, 統一法律見解); search_law for the text of statutes. Read a result in full with get_decision_details (by its jid).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default 15) | |
| query | Yes | Search query: a legal concept, fact pattern, or keywords in Traditional Chinese, e.g. "車禍 過失責任" or "勞資 資遣費 認定". | |
| case_types | No | Optional case-type filter: any of C (憲法), V (民事), M (刑事), A (行政), P (懲戒). Omit for no restriction. Set it only when the user explicitly asks to limit the case type -- do NOT infer it from words in the query: terms like 民事/刑事/行政/懲戒 appear constantly in ordinary topical queries with no filtering intent. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover safety (readOnly, idempotent, non-destructive), and the description adds substantive behavior beyond them: the ranking mechanism (hybrid semantic + keyword, reranked), the non-exhaustive result contract, and the exact court-tier coverage boundary. These are the traits an agent most needs to know to avoid treating results as a complete match set.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads purpose and scope in the first two sentences, then routing rules, then the read-in-full pointer. It is dense but every clause carries routing or behavioral information; nothing is filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, yet the description tells the agent what results look like (relevant ranked subset, not all matches) and how to continue reading one (get_decision_details by jid). Combined with full schema coverage and annotations, an agent has everything needed to call and follow up correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all three parameters are already documented, including the case_types caveat about not inferring filters from query wording. The description adds essentially no parameter-level detail beyond the schema (e.g., no guidance on sensible limit values or query phrasing), so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Search Taiwan (ROC) court decisions') and immediately narrows the scope to a named apex-tier set of courts, explicitly excluding district and high court decisions. This lets an agent separate it from search_decisions_exact and search_law without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives concrete trigger questions ('how have courts ruled on X'), states the negative condition ('it returns the most relevant decisions, not every match'), and names three alternatives with the exact condition that selects each (search_decisions_exact for exhaustive literal matches/counts, search_grand_chamber_decisions for divergent-panel interpretation questions, search_law for statutes), plus the follow-up get_decision_details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 1-based page number (default 1). Results are newest first. | |
| court | No | Optional 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). | |
| terms | Yes | Literal 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`. | |
| exclude | No | Optional literal phrases that must NOT appear in the decision, e.g. ["租賃"]. terms + exclude: at most 6 in total. | |
| year_to | No | Optional: latest decision-date year, inclusive; same format as year_from. | |
| page_size | No | Decisions per page (default 20, max 50). | |
| year_from | No | Optional: 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_types | No | Optional case-type filter: any of C (憲法), V (民事), M (刑事), A (行政), P (懲戒). Omit for no restriction. |
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.
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.
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.
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.
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.
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.
Search Taiwan (ROC) Grand Chamber (大法庭) rulings -- the mechanism Taiwan's Supreme Court (最高法院, 民事/刑事) and Supreme Administrative Court (最高行政法院) use to resolve DIVERGENT legal interpretations across panels (統一法律見解／法律見解歧異) and set binding precedent on a pure point of law. Use this instead of search_decisions when the question is specifically about which legal interpretation prevails on a disputed point, not an ordinary fact-pattern search. Covers the full thread for each question -- the referring panel's order, the Grand Chamber's binding answer, and the originating case's final judgment applying it -- across civil, criminal, and administrative matters. Backed by the same Qdrant hybrid search as search_decisions, scoped to this curated subset (~225 decisions) instead of the full apex-tier corpus.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default 15) | |
| query | Yes | Search query: the legal question or point of law in Traditional Chinese, e.g. "未遂犯與既遂犯的區分標準" or "借名登記契約的效力". Use this tool specifically for questions about unifying or resolving DIVERGENT legal interpretations across panels (統一法律見解／法律見解歧異) -- not for an ordinary fact-pattern search, which search_decisions covers over a much larger corpus. | |
| case_types | No | Explicit case-type filter: V (民事), M (刑事), A (行政). Omit for no restriction. Set it only when the user explicitly asks to limit the case type -- do NOT infer it from words like 民事/刑事/行政 in the query. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description's job is added context — and it delivers: corpus scope (~225 decisions), backing infrastructure (same Qdrant hybrid search), and the fact that each result spans the full thread (referring order, binding answer, final judgment). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads the scope and the routing rule, and each sentence carries information. It is somewhat dense with repeated parentheticals (統一法律見解／法律見解歧異 restated in the schema query field), which costs a point but does not obscure the message.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no output schema, the description supplies everything needed to call and interpret it correctly: scope, corpus size, the semantic shape of returned results, and the sibling alternative. Nothing material is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so query, limit, and case_types are already well documented in the schema — including the caveat not to infer case_type from query wording. The description adds no parameter-level syntax or format detail beyond that, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource (search Grand Chamber rulings) and immediately scopes it to Taiwan's 大法庭 mechanism for resolving divergent interpretations. It names the sibling it is not (search_decisions) and the distinguishing question type, so an agent can route without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly gives the selection rule: use this instead of search_decisions when the question is about which legal interpretation prevails on a disputed point, not an ordinary fact-pattern search. The when-not condition is stated, not inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Taiwan (ROC) law articles by legal concept, keyword, or a specific article reference. Backed by a Qdrant hybrid (dense + bigram-sparse, RRF-fused) search over a curated set of major Taiwan laws and regulations (laws only, not court decisions).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default 5) | |
| query | Yes | Search query: a legal concept or keywords in Traditional Chinese, e.g. "民法 侵權行為" or "勞動基準法 資遣費". A specific article reference like "民法第184條" also works. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/idempotent/non-destructive/openWorld=false, so the description's added value is the corpus boundary (curated major Taiwan laws, excluding court decisions) and the retrieval mechanism (Qdrant hybrid dense + bigram-sparse RRF). It omits result format and pagination behavior, keeping it below 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler, with the primary purpose front-loaded and the corpus caveat second. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter search tool with no output schema and rich annotations, the description covers corpus scope and matching behavior adequately; only the shape of returned results is left implicit, which is a minor gap given the annotations already convey the safety profile.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema description already explains query syntax and the limit default, so the description merely restates that a concept, keyword, or article reference is acceptable. Baseline 3 applies when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (Search) plus resource (Taiwan ROC law articles) and scope (laws only, not court decisions), which directly distinguishes it from sibling search_decisions and get_article_by_number. An agent can route without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by naming query modes (legal concept, keyword, article reference) and limiting the corpus, but never states when to prefer search_law_titles or get_article_by_number over this tool, nor when-not to use it. Usage is inferable but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Resolve a full or partial law name to the official law name(s) in the corpus. Call this first when you are unsure of the exact name a law is filed under before using get_article_by_number / get_law_hierarchy / get_law_details.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A full or partial law name in Traditional Chinese, e.g. "個人資料" or "營業秘密". Returns matching official law names. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, closed-world, and non-destructive, so the safety profile is fully covered without the description. The description adds the resolution-workflow context, but says nothing about ambiguity handling (which match wins when several official names return) or the shape of returned matches.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero filler, with the core purpose front-loaded and the routing guidance immediately after. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only lookup with no output schema, the description adequately conveys that matching official law names are returned. It could go slightly further on multi-match return format, but nothing essential to invoking it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and there is only one parameter, whose schema entry already documents Traditional Chinese input and gives examples. The description adds nothing beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb and resource: resolving a full or partial law name to official law name(s) in the corpus. It is clear what the tool returns, though it never explicitly distinguishes itself from the similarly named sibling `search_law`, which an agent could easily confuse it with.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It states the exact condition for use ("Call this first when you are unsure of the exact name a law is filed under") and names the three downstream tools that depend on its output, effectively routing the agent through the workflow. This is explicit when-to-use plus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
search_decisions1 field changedInput schema / properties / case_types / descriptionPrevious value: -"Explicit case-type filter: any of C (憲法), V (民事), M (刑事), A (行政), P (懲戒). Omit for no restriction -- do NOT infer this from words in the query text (see decisions_qdrant.rs's search_decisions_qdrant module comment for why: case-type words like 民事/刑事/行政/懲戒 show up constantly in ordinary topical queries with no filtering intent)."New value: +"Optional case-type filter: any of C (憲法), V (民事), M (刑事), A (行政), P (懲戒). Omit for no restriction. Set it only when the user explicitly asks to limit the case type -- do NOT infer it from words in the query: terms like 民事/刑事/行政/懲戒 appear constantly in ordinary topical queries with no filtering intent."search_grand_chamber_decisions1 field changedInput schema / properties / case_types / descriptionPrevious value: -"Explicit case-type filter: V (民事), M (刑事), A (行政). Omit for no restriction -- do NOT infer this from words in the query text (same reasoning as search_decisions's case_types param)."New value: +"Explicit case-type filter: V (民事), M (刑事), A (行政). Omit for no restriction. Set it only when the user explicitly asks to limit the case type -- do NOT infer it from words like 民事/刑事/行政 in the query."get_article_by_numberget_decision_detailsget_law_detailsget_law_hierarchysearch_decisionssearch_decisions_exactsearch_grand_chamber_decisionssearch_lawsearch_law_titlesTaiwan legal research: court judgments, statutes, and interpretations. 台灣判決、法條、函釋、釋字搜尋。
Taiwan government tenders: 14M+ notices, vendor/agency intel, forecasts, committees, debarment list.
Taiwan legal research MCP: 判決書、全國法規、釋字/憲判與立法歷程查詢，12 個工具，回應均附官方出處 URL。
Citation-guarded retrieval over 22M Taiwan court judgments and administrative interpretations
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