Korean Law MCP

Server Quality Checklist

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

Latest release: v0.4.4
Disambiguation5/5
Each tool has a clearly distinct purpose, with no overlapping functions. Compare_old_new, explore_legal_chain, get_article_history, and search variants handle different aspects of legal research without ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., compare_old_new, explore_legal_chain, read_legal_resource, search_korean_law), with no mixing of styles.
Tool Count5/5
With 10 tools, the server is well-scoped for a legal research assistant. Each tool provides essential functionality without being excessive or insufficient.
Completeness4/5
The tool set covers searching, reading, comparison, history, attachments, terms, interpretations, and external links. A minor gap is the inability to retrieve the full text of a specific older version directly, but agents can work around this using revision history and comparison.
Average 3.8/5 across 10 of 10 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on December 15, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only discloses that it returns a list of terms with IDs; no side effects, permissions, or other behavioral traits are mentioned. This is insufficient for a search tool.
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 very short (two sentences) but not necessarily concise in a helpful way—it omits important details while being brief. It is front-loaded with the purpose, but could add parameter guidance without losing conciseness.
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 simple input (one string) and presence of an output schema (so return docs are covered), the description is minimally adequate. However, it does not differentiate well from sibling search tools, which share similar structures.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'query' is entirely undocumented in both schema (0% coverage) and description. The description implies the query is a legal term to search, but adds no explicit details about format or constraints.
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 verb ('Search for legal terms') and the resource ('definitions'), and mentions the return type ('list of matching terms with IDs'). It distinguishes from sibling search tools by specifying 'legal terms' as opposed to laws, articles, or interpretations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings. The description only states what it does, without indicating scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral transparency. It only mentions the source authority but lacks details on limitations, pagination, or effects. Minimal disclosure.
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 one short sentence with a parenthetical, front-loading the key action and resource. No unnecessary words.
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 tool's simplicity and existence of an output schema, the description provides the essential purpose. However, it could offer more detail on query expectations or result scope for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage, and the description does not add any meaning to the 'query' parameter beyond its literal interpretation. No format, examples, or constraints are given.
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 verb 'Search' and the resource 'statutory interpretations', and specifies the source 'by Ministry of Government Legislation'. This distinguishes it from sibling tools like search_korean_law.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs alternatives, nor any exclusions or prerequisites. It simply states what it does without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral transparency. It only states 'Get a list' implying a read operation, but fails to disclose any side effects, authentication needs, rate limits, or output characteristics. The presence of an output schema is not referenced, so the description adds minimal behavioral context beyond the basic action.
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 and well-structured: a clear one-line purpose statement followed by a brief parameter description. Every sentence is necessary and no fluff is present. It is front-loaded with the key action.
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 tool's simplicity (single parameter) and the presence of an output schema, the description is minimally complete. It adequately explains the tool's purpose and parameter. However, it lacks usage guidelines, behavioral details, and any reference to the output, making it only adequate for a simple 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?
The schema description coverage is 0%, but the description adds meaningful semantics for the law_id parameter by providing examples ('12345' or 'statute:12345'). This helps clarify the expected format, which the schema alone (type string) does not. It adds value beyond the raw 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?
The description clearly states the tool gets a list of attached forms and tables (별표/서식) for a specific statute, using a specific verb and resource. However, it does not explicitly differentiate from sibling tools like read_legal_resource or search_law_articles, which might also retrieve statute-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, limitations, or when not to use it. The absence of such context leaves the agent without decision-making cues for tool selection among siblings.
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?
No annotations exist, so the description must carry full behavioral transparency. It states that the tool returns 'Markdown formatted text' but does not disclose whether it is read-only, any side effects, or edge cases. The read-only nature is implied by 'search' but not explicit.
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 compact with a clear purpose statement, then structured Args and Returns sections. No redundant sentences, but could be slightly more concise by omitting 'Args:' label.
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 output schema, the description provides the core functionality but lacks details on empty results, error handling, or when the tool is inappropriate. It is minimally adequate.
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?
Despite 0% schema description coverage, the description adds meaning to both parameters: law_id includes an example format, and keywords specifies 'space-separated'. This goes beyond the bare schema titles.
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 searches for keywords within statute articles, with a specific verb and resource. It distinguishes from siblings like 'search_legal_terms' by specifying 'articles of a statute', though it does not explicitly contrast alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs. sister tools like 'compare_old_new' or 'search_statutory_interpretations'. There is no mention of prerequisites or conditions for use.
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?
No annotations provided, so description carries full burden. It discloses return format (markdown document with connected texts) but does not mention side effects, performance, or access requirements.
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?
Well-structured with bullet points, concise yet informative. Every section serves a purpose.
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 presence of an output schema (not shown), description adequately explains the three-part return structure, making it sufficient for understanding the tool's behavior.
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 0% coverage, but description adds meaning by providing concrete query examples (e.g., 'Higher Education Act Article 20'), compensating for the minimal 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?
Description clearly states 'Perform a Deep Search (Legal Graph)' and lists three specific outputs. It implies a broader scope than sibling search tools, though not explicitly differentiating them.
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?
Provides explicit when-to-use ('when you want to understand the full context') and example queries, but lacks when-not-to-use or direct comparisons to similar sibling tools.
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?
No annotations are provided, so the description carries the full burden. It states the tool returns a Markdown comparison, implying a read operation. However, it does not disclose potential requirements (e.g., latest amendment existence), auth needs, or error handling. Adequate but could be more transparent.
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 three sentences plus a bulleted usage list and Args/Returns. It is front-loaded with the core purpose. Could be slightly tighter, but no wasted words.
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 single parameter and the existence of an output schema, the description adequately explains what the tool does and returns. It covers the main use cases and return format. Missing potential details like default behavior or error conditions, but still fairly complete.
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 only one parameter with 0% description coverage. The description compensates by explaining the parameter accepts law name or ID, providing examples (e.g., '고등교육법', 'statute:12345'), which adds significant semantic value beyond the schema's bare type definition.
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 gets the old/new article comparison for a law and shows changes in the most recent amendment. It uses a specific verb ('get') and resource ('comparison'), and the mention of side-by-side comparison distinguishes it from siblings like 'get_article_history' which shows revision history.
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 includes a bulleted list of when to use the tool: seeing exact changes, comparing before/after, and understanding revision scope. This provides clear context, though it does not explicitly state when not to use it or suggest alternatives.
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?
No annotations provided, so description carries full burden. It states the output is Markdown formatted links. No side effects mentioned, but as a link generator, it is inherently read-only and non-destructive. Transparency is adequate.
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?
Description is front-loaded with purpose, then bullet points for use cases, then structured Args/Returns. It is well-organized and not overly verbose, though slightly more concise could be achieved.
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?
With one parameter and an output schema, the description covers the tool's functionality sufficiently. It explains the return format and the parameter meaning. No gaps noted for this simple 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 0%, but description compensates by explaining the resource_id parameter in detail, including the format and that it comes from search results, which adds meaning beyond the schema's simple string type.
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 generates external links to the National Law Information Center. It lists specific use cases and is distinct from sibling tools like search and read tools, which focus on retrieving content rather than generating links.
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?
Explicitly states when to use the tool (view original source, share direct link, access additional features). Does not mention alternatives or when not to use, but the context is clear enough for appropriate 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?
No annotations were provided, so the description must fully disclose behavior. It explains Reference Resolution (auto-appending referenced law content) and Robustness (fallbacks for outdated IDs). It also states the return format is Markdown with metadata, body, and resolved references. This provides good transparency beyond the input schema.
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 concise and well-structured. It opens with a clear purpose statement, then organizes additional details into bullet points for Features and Return. Every sentence adds value, and there is no repetition or unnecessary content.
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 (full text retrieval with reference resolution and fallback logic) and the presence of an output schema, the description covers key aspects: ID format, source of ID, feature highlights, and return format. It could be slightly more explicit about the relationship with siblings, but overall it provides sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for the single parameter resource_id, but the description adds substantial meaning: strict format 'type:id' with examples (statute:12345, prec:98765), and notes the ID comes from search_korean_law output. This fully clarifies what the parameter expects.
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 'Reads the full content of a specific legal resource using its Typed ID' with specific examples of resource IDs (statute:12345) and mentions full text retrieval, reference resolution, and robustness. This provides a clear and distinct purpose from sibling tools like search_korean_law which returns IDs for use with this tool.
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 indicates that the resource_id comes from search_korean_law output and that reference resolution saves extra round-trips, implying when to use. However, it lacks explicit guidance on when not to use this tool versus alternatives like compare_old_new or explore_legal_chain, or any prerequisites.
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?
No annotations provided, so description carries full burden. Describes return format ('Markdown formatted list') and essential behavior (historical amendments). Does not disclose authentication or rate limits, but for a read-only history tool, this is adequate.
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 short paragraphs plus Args/Returns sections. Every sentence adds value. Front-loaded with purpose. No fluff.
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 one parameter and simple functionality, description fully covers what the tool does, when to use, how to use (parameter examples), and what to expect (Markdown output). No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter 'law_name_or_id' is well-described with examples (name, ID formats). Schema only provides 'string' and title; description adds critical usage context, compensating for 0% schema description coverage.
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 clearly states 'Get the revision history of a law' with specific verb and resource. Lists concrete use cases (e.g., 'Know when a law was last amended') that distinguish it from sibling tools like search_korean_law or read_legal_resource.
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?
Explicitly provides 'when the user wants to' scenarios. Does not explicitly mention when not to use or alternatives, but the use cases are sufficiently narrow to imply context. Slight deduction for no exclusion 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?
Despite no annotations, the description fully discloses behavior: for article lookup it returns exact content directly, for broad search it returns a summarized list with Typed IDs for further steps. It also mentions 'Smart Search' adaptation, leaving no ambiguity about what the tool does.
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 well-structured with sections, bullet points, and usage tips. Every sentence adds value without redundancy. It is appropriately sized for the tool's complexity.
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 single parameter, no annotations, and presence of an output schema, the description covers all needed context: capabilities, input examples, output format, and links to sibling tools. It is complete for the tool's purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no description for the query parameter (0% coverage), but the description adds extensive meaning through examples and behavior explanation. It clarifies how queries are interpreted (specific article vs broad) and provides exact input formats.
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 is the primary interface for searching Korean laws, precedents, and administrative rules, and distinguishes between specific article lookup and broad keyword search. It also differentiates from sibling tools like search_law_articles by noting a separate use case.
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?
Explicitly advises when to use specific article lookup vs broad search, provides tips like 'ALWAYS try to be specific', and directs users to search_law_articles for finding keywords within a law. This gives clear usage context and alternatives.
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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