Skip to main content
Glama
Tech-curator

Korean Patent MCP

by Tech-curator

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: one retrieves patents citing a specific patent, another gets detailed information for a single patent, and the third searches patents by applicant name. The descriptions clearly differentiate these functions, making tool selection unambiguous for an agent.

    Naming Consistency5/5

    All tool names follow a consistent 'kipris_verb_noun' pattern (kipris_get_citing_patents, kipris_get_patent_detail, kipris_search_patents). This uniform naming convention makes the tool set predictable and easy to understand, with no deviations in style or structure.

    Tool Count3/5

    With only 3 tools, the set feels thin for a patent domain that typically involves more operations like updating, deleting, or analyzing patents. While the tools cover basic retrieval and search, the count is borderline low for comprehensive patent management, limiting agent capabilities.

    Completeness2/5

    The tool surface has significant gaps for a patent server, lacking essential operations such as creating, updating, or deleting patents, as well as advanced features like patent analysis or filtering by other criteria. This incompleteness will likely cause agent failures in handling full patent workflows.

  • Average 3/5 across 3 of 3 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
  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 of behavioral disclosure. It states the tool retrieves data (조회), implying a read-only operation, but does not address permissions, rate limits, error handling, or response behavior beyond the response_format parameter. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

    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 well-structured: a clear purpose statement followed by an 'Args' section listing parameters with brief explanations. It avoids redundancy and is front-loaded with the main function. However, the 'Args' section could be integrated more smoothly into the narrative.

    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 has an output schema (which should cover return values), the description's focus on purpose and parameters is adequate. However, with no annotations and low schema coverage, it lacks details on behavioral aspects like error cases or performance. It meets minimum viability but has clear gaps in context for safe and effective use.

    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?

    The description adds some semantic context: it explains that application_number is for the '기준 특허' (base patent) and is required, and response_format specifies the output format with options. However, schema description coverage is 0%, so parameters are undocumented in the schema. The description compensates partially by clarifying meanings but does not fully detail constraints or examples, such as the format of application_number.

    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's purpose: '특정 특허를 인용한 후행 특허들을 조회합니다' (Retrieve patents that cite a specific patent). It specifies the verb (조회/retrieve) and resource (후행 특허들/citing patents), and distinguishes from siblings like kipris_get_patent_detail (which gets details) and kipris_search_patents (which searches). However, it doesn't explicitly contrast with siblings beyond the inherent difference in function.

    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 sibling tools or other contexts, nor does it specify prerequisites or exclusions. The usage is implied by the purpose but lacks explicit direction.

    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 behavioral disclosure. It states it's a retrieval operation ('조회합니다'), implying read-only behavior, but doesn't address permissions, rate limits, error handling, or response structure. The mention of response formats ('markdown' or 'json') hints at output behavior, but overall, it lacks critical details for a tool with no annotation coverage.

    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 appropriately sized and front-loaded, with the purpose stated first, followed by parameter details. It uses two sentences efficiently, with no redundant information. However, the parameter section could be slightly more integrated into the flow, but overall, it's concise and well-structured.

    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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and parameters but lacks usage guidelines and behavioral context. The presence of an output schema reduces the need to explain return values, but without annotations, more behavioral details would improve completeness for a retrieval tool.

    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?

    The description adds some semantic value beyond the input schema, which has 0% description coverage. It explains 'application_number' as '출원번호 (필수, 예: '1020200123456')' (application number, required, example) and 'response_format' as '응답 형식 ('markdown' 또는 'json')' (response format, 'markdown' or 'json'), providing meaning and examples. However, it doesn't fully compensate for the schema's lack of descriptions, as it misses details like format constraints or default behavior for 'response_format'.

    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's purpose: '출원번호로 특허의 상세 정보를 조회합니다' (Retrieve detailed information of a patent by application number). It specifies the verb '조회합니다' (retrieve) and resource '특허의 상세 정보' (detailed patent information), making the action and target explicit. However, it doesn't differentiate from sibling tools like 'kipris_search_patents' (which likely searches broadly) or 'kipris_get_citing_patents' (which retrieves citing patents), so it misses full sibling distinction.

    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 doesn't mention sibling tools like 'kipris_search_patents' for broader searches or 'kipris_get_citing_patents' for related patents, nor does it specify prerequisites, exclusions, or contextual cues. Usage is implied only by the purpose statement, lacking explicit when/when-not instructions.

    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 behavioral disclosure. It mentions pagination (page, page_size) and response format options, which are useful. However, it lacks critical details like rate limits, authentication needs, error handling, or whether this is a read-only operation. For a search tool with 5 parameters, this leaves significant behavioral gaps.

    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 appropriately sized and front-loaded: the first sentence states the purpose, followed by a structured parameter list. Each sentence earns its place by providing essential details. It could be slightly more concise by integrating the parameter explanations more seamlessly, but overall it's efficient.

    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 moderate complexity (5 parameters, 1 required), no annotations, and the presence of an output schema, the description is reasonably complete. It covers all parameters with semantics and examples, and the output schema likely handles return values. However, it lacks behavioral context like rate limits or error cases, which slightly reduces 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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter in Korean with examples (e.g., '삼성전자' for applicant_name), default values, and constraints (e.g., '최대: 100' for page_size). This compensates well for the schema's lack of descriptions, though it doesn't fully clarify the 'status' parameter's enum values beyond the listed codes.

    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's purpose: '출원인명으로 한국 특허를 검색합니다' (Search Korean patents by applicant name). It specifies the verb ('검색합니다' - search) and resource ('한국 특허' - Korean patents), though it doesn't explicitly differentiate from sibling tools like 'kipris_get_citing_patents' or 'kipris_get_patent_detail' beyond the search focus.

    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 its siblings. It mentions no alternatives, exclusions, or contextual prerequisites. The only implied usage is for searching patents by applicant name, but this is redundant with the purpose statement and offers no decision-making help.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

korean-patent-mcp MCP server

Copy to your README.md:

Score Badge

korean-patent-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Tech-curator/korean-patent-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server