Korean Law MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v4.9.1
Disambiguation4/5
Most tools have distinct purposes, but search_law and search_decisions could be confused; both involve searching but one for laws and one for decisions. Additionally, discover_tools and execute_tool are meta-tools that might be misused if not understood.
Naming Consistency4/5
All tool names use snake_case with a verb_noun pattern (e.g., search_law, get_law_text). However, 'ordinance_radar' is a noun_noun compound and 'legal_research'/'legal_analysis' are less descriptive of action, introducing slight inconsistency.
Tool Count5/5
With 10 tools, the server covers core legal operations (search, retrieve, analyze, research) without being bloated. The count is well-scoped for the domain.
Completeness5/5
The tool set is comprehensive for a read-only legal database: search, retrieval, annexes, research, analysis, and decision lookup. The meta-tools (discover_tools, execute_tool) extend capabilities. No obvious gaps for common legal tasks.
Average 4.1/5 across 10 of 10 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint as false. The description adds that it is an integrated search with domain-specific options, which is consistent and provides additional context, but does not significantly extend beyond the 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?
The description is concise with two sentences, but the first sentence is a mix of Korean and English, which may reduce clarity for some agents. It front-loads the key purpose but could be structured more cleanly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, nested 'options' object, no output schema), the description covers the main use cases and provides examples, but it does not fully explain all domain-specific option structures or the return format. More detail could improve completeness.
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?
The input schema has 100% description coverage for parameters. The description adds value by explaining the meaning of the 'domain' parameter with examples (e.g., 'nts' for tax), and hints at usage of 'options' for certain domains. This enriches the schema beyond the baseline.
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?
The description clearly states it is an integrated search across 18 domains, with specific domains listed in the enum. It mentions selecting a domain and gives an example for precedents. However, it does not explicitly distinguish itself from the sibling 'search_law' tool, which may have overlapping functionality.
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?
The description provides usage guidance for specific domains (e.g., 'precedent' with options.includeText, 'nts' for tax inquiries). It implies that the tool covers multiple legal domains but lacks explicit when-to-use vs. alternatives. No exclusions or comparisons to sibling tools are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. Description adds that full=false abbreviates text and certain sections are always full, providing some behavioral context beyond annotations. However, the abbreviation mechanism ('계단식 축약') is not explained, and no output format is described.
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?
Extremely concise single sentence, front-loaded with core purpose ('통합조회', domain+id), and efficiently explains key parameter behavior. No redundant information; every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Schema descriptions cover all parameters, annotations exist, but no output schema. Description lacks details about return format, error handling, or pagination. For a retrieval tool, this leaves the agent guessing about the response structure.
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 baseline is 3. Description adds meaningful detail: explains full parameter's abbreviation behavior (including which sections are always full) and provides domain-specific option examples for options parameter, adding value beyond schema descriptions.
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?
The description clearly states the tool retrieves full text from 18 domains using domain and ID, and mentions abbreviation behavior. However, it uses Korean terms and is slightly ambiguous for non-Korean speakers.
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?
No explicit guidance on when to use this tool versus siblings like search_law or get_law_text. It implicitly suggests requiring domain and ID from search results, but lacks direct 'when to use' or 'when not to use' instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 agent knows this is a safe read operation. The description adds context about being a fallback category search but does not detail rate limits, pagination, or other behavioral aspects. With good annotations, the description provides moderate additional value.
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?
The description is extremely concise—a single sentence that includes the tool name, usage context, and example categories. Every word is informative, and the structure is front-loaded with the key purpose. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with one parameter and no output schema, the description explains when to use it and what to input. However, it does not describe what the tool returns (e.g., list of tool names or descriptions). This omission reduces completeness for an agent that needs to know the output format.
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?
The single parameter 'intent' has a clear description in the schema (search intent/category with examples). The tool description reinforces this by listing example categories. Since schema description coverage is 100%, the description adds extra context beyond the schema, earning a 4.
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?
The description states it is a fallback tool for when other tools don't work and allows category-based search for specialized legal tools (80+ categories). While it clearly indicates the tool's purpose, it could be more specific about the output (e.g., list of tool names). It differentiates from siblings by being a meta-discovery tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says '위 도구로 안 되는 경우' (when the above tool doesn't work), providing a clear condition for use. It also lists example categories, guiding the user on what to input. No explicit when-not-to-use, but the context implies using specific sibling tools when applicable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds proxy execution context but no additional behavioral details (e.g., error handling). Meets minimum 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words. Includes essential information and is front-loaded with the purpose. Highly efficient.
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?
Given the simplicity of the tool, the annotations, and the clear schema, the description is complete. It covers the core functionality without needing to detail return values or complex behavior.
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?
Schema coverage is 100%, so description need not add much. It simply restates 'tool_name + params', offering no additional meaning beyond the schema. Baseline score of 3 is appropriate.
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?
The description explicitly states it is a proxy execution for tools discovered via discover_tools, clearly identifying the verb 'executes' and the resource 'discovered tools'. It distinguishes from sibling tools which are specific law tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after discover_tools by referencing it, providing clear context. However, it does not explicitly state when not to use or mention alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and non-destructive. The description adds that the tool extracts content from appendices and mentions that amounts/standards are often found there, providing useful behavioral context 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?
Two concise sentences with no wasted words. Each sentence adds distinct value: purpose and usage tip.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the output format or structure, which is a gap given no output schema. It could be more complete about what 'content extraction' returns (e.g., plain text, structured data).
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?
All parameters are described in the schema (100% coverage). The description adds value by showing a usage pattern (lawName+'별표N') and hinting that lawName can include appendix number directly, which is not in the schema.
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?
The description clearly states the tool retrieves appendices and forms (별표/서식) from Korean law, using lawName combined with appendix number. This is a specific verb-resource pair and distinguishes it from siblings like search_law or get_law_text.
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 provides a usage hint (combine lawName with appendix number) and notes that amounts/standards are often in appendices, implying when to use. However, it does not explicitly exclude alternatives or compare with sibling tools, leaving the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the requirement for mst/lawId and optional jo, but does not expand on behavioral details such as return format or response structure.
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?
The description is a concise single sentence covering purpose and key parameters. It is efficiently front-loaded but could benefit from a slightly more structured format (e.g., separating purpose from requirements).
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?
Given the tool's straightforward purpose (retrieve law text), the annotations cover safety, and the schema covers all parameters, the description provides sufficient context. No output schema exists, but return values are self-evident for a retrieval tool.
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 parameters are well-described in the schema. The description adds value by clarifying that mst and lawId are essential (even though the schema marks no required fields), and jo is optional for filtering to a specific provision.
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?
The description clearly states it retrieves the full text of legal provisions (조문 전문 조회), specifies that mst/lawId are required, and mentions optional filtering to a specific provision (jo). This distinguishes it from siblings like search_law (which searches laws) and get_decision_text (court decisions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states that mst/lawId are required and jo is optional, implying usage after obtaining a law ID from search_law. However, it does not provide explicit when-not or alternatives guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, etc.) align with the description, which adds useful behavioral context: 'LLM 환각 방지' (hallucination prevention), '한국형 Citator', and mode-specific behaviors like deep scanning. No contradictions; description enhances understanding 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense block but well-structured with mode markers and explanations. Each sentence adds value, covering four modes efficiently. Slightly verbose due to mode details, but necessary for completeness.
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?
Despite no output schema, the description comprehensively explains all 11 parameters across four modes, including defaults and optional flags. It covers every mode's purpose, inputs, and behavioral nuances, making the tool fully understandable.
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% with detailed parameter descriptions. The description further groups parameters by mode, clarifying which are required for each analysis type. This adds organizational value beyond the schema alone.
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?
The description clearly states the tool's purpose: integrated verification and analysis for Korean law with four distinct modes. Each mode is explicitly defined (e.g., 'verify_citations', 'cite_check'), distinguishing it from sibling tools like search_law or get_law_text which focus on retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use each mode by listing required parameters and function (e.g., 'verify_citations' for checking citations in text, 'cite_check' for case law citation tracking). It does not explicitly exclude cases or mention alternatives, but the mode-based structure provides clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are already provided and consistent. The description adds behavioral context: it automatically extracts citations, compares dates, and flags potential issues. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single long sentence that is dense with information. While it is front-loaded with the purpose, it could be more concise or broken into two sentences for readability.
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?
The description covers the core functionality and inputs. However, it lacks details about the output format or how results are presented, leaving some gaps for a tool that produces a comparison and flagging result.
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 covers all 4 parameters with descriptions (100% coverage). The description adds value by explaining aliases (id for ordinSeq, query for ordinanceName) and the relationship between parameters, though the schema already provides basic clarity.
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?
The description clearly states the tool's purpose: extracting parent laws cited by ordinances, comparing implementation dates, and flagging potential revision needs. It is specific and distinguishes from sibling tools like search_law (which searches laws) and legal_analysis (which provides general analysis).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly targets ordinance officers for tracking parent law revisions and provides that one of id/ordinSeq or ordinanceName must be specified. It does not explicitly state when not to use it, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent), the description discloses auto-fallback to local ordinances/administrative rules on zero results, automatic abbreviation conversion, and automatic notation of title changes and pending amendments. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose. It could benefit from bullet points but contains no wasted sentences.
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?
Despite no output schema, the description explains return values (lawId, mst) and handles edge cases (zero results, abbreviation conversion, title changes). Complete enough for an agent to use effectively.
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 baseline is 3. The description adds context: query examples, display default value and its effect on exact matching, improving usability over schema alone.
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?
The description clearly states the tool's purpose: keyword search for Korean laws, ordinances, and administrative rules to obtain identifiers (lawId, mst). It distinguishes from sibling tools like get_law_text and search_decisions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage guidance: use before querying law/ordinance/rule texts for identifier acquisition. It explains automatic fallback and abbreviations, though it does not explicitly state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds significant behavioral context: parallel API calls, multi-step research, and being a composite tool. No contradictions.
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?
The description is front-loaded with the main purpose and structured as a task list. While verbose, every sentence adds unique value for a 12-parameter tool. Could be slightly shorter but effective.
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?
Given the tool's complexity (12 parameters, enums, multiple tasks) and full schema coverage, the description is comprehensive. Missing output format details, but no output schema exists. Overall adequate for agent selection.
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%. The description adds extensive semantics beyond parameter descriptions: explaining task-scenario compatibility, parameter usage contexts (e.g., [document_review required]), and default behaviors, enriching understanding.
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?
The description clearly states the tool integrates multiple APIs for complex Korean legal research, and distinguishes itself from simpler sibling tools like search_law and get_law_text by explicitly advising to use those for single-lookup queries.
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 provides explicit when-to-use guidance (complex questions requiring multiple APIs) and when-not-to-use (single-lookup cases), and lists specific task types with examples, enabling appropriate selection.
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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