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woongaro

Legal Search MCP

by woongaro

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    search-law and get-law-detail have clearly distinct roles: the former finds laws by query, the latter retrieves specific articles by ID. No overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with hyphen separation: search-law and get-law-detail. Predictable and readable.

    Tool Count3/5

    Only two tools, which is on the thin side. While they cover the essential search-and-retrieve workflow, the small number may limit broader legal research tasks.

    Completeness5/5

    For a legal search server, the two tools provide a complete workflow: search for relevant statutes and retrieve their detailed provisions. No evident dead ends.

  • Average 3.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
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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?

    With no annotations, the description must disclose behavioral traits, but it only states the basic function. It implies a read-only lookup ('조회') but does not mention any side effects, error conditions, authentication requirements, or rate limits.

    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 two short sentences, front-loaded with the core action, and contains no filler or redundant information. Every word contributes to clarifying the tool's purpose.

    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 tool has four parameters with conditional requirements and no output schema. The description gives a high-level overview but omits usage context such as the need to have a lawId from search-law or the conditional need for efYd. It is adequate but relies heavily on the schema for operational details.

    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 description coverage is 100%, so the baseline is 3. The description adds minimal meaning by mentioning the two identifier modes (lawId or mst), but does not elaborate on conditional requirements (e.g., efYd required with mst) beyond what the schema already provides.

    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 detailed content (articles) of a specific law, using a law ID or master number. It provides a specific verb (조회/retrieve) and resource (법령 상세 내용), but does not explicitly contrast with the sibling tool 'search-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 gives no guidance on when to use this tool versus 'search-law'. It only states what it does and the lookup methods, without mentioning prerequisites (e.g., use search-law first) or when to prefer one identifier over the other.

    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 the full burden of disclosing behavioral traits. It only mentions the ability to search by name or content, which is already present in the searchType parameter of the schema. It does not disclose whether the operation is read-only, the response format, pagination behavior, or any side effects. This is a minimal disclosure that adds little beyond the 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 two short, clear sentences in Korean. It front-loads the core purpose and uses no unnecessary words. Every word earns its place, making it highly concise and well-structured.

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

    Completeness2/5

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

    Given the tool has 7 parameters, 3 enums, no annotations, and no output schema, the description is too sparse. It does not explain the typical search workflow (e.g., that results can be followed up using get-law-detail), nor does it clarify return behavior or default settings. The description only conveys the basic search capability, leaving the agent without enough context for effective invocation.

    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 description coverage is 100%, so all 7 parameters are already documented with descriptions. The tool description adds no additional parameter semantics beyond restating that searching by law name or content is possible, which mirrors the searchType parameter. The baseline of 3 applies because the schema does the heavy lifting.

    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 searches Korean laws and specifies the two search modes (by law name or content). This is a specific verb+resource pairing and implicitly distinguishes it from the sibling tool get-law-detail, which presumably retrieves details of a specific law.

    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 implies the tool should be used for searching laws, but it provides no explicit guidance on when to prefer this tool over get-law-detail, nor does it mention any exclusions or prerequisites. The context signal shows get-law-detail as a sibling, but the description itself does not reference it.

    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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