KIPRIS Plus MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: kipris_bibliography retrieves detailed bibliographic information for a single patent, kipris_build_query converts natural language to a search query, kipris_cpc_search searches by CPC code, kipris_free_search performs keyword searches across all fields, kipris_item_search allows field-specific searches, and kipris_search handles keyword searches in patent text. There is no overlap or ambiguity between these functions.
Naming Consistency5/5
All tool names follow a consistent pattern: they start with 'kipris_' followed by a descriptive term (e.g., bibliography, build_query, cpc_search). This uniform prefix and snake_case structure make the tools easily identifiable and predictable.
Tool Count5/5
With 6 tools, this server is well-scoped for patent search and retrieval. It covers key operations like detailed lookups, query building, and various search methods (CPC, free, item-specific), which is appropriate for the domain without being overwhelming or insufficient.
Completeness4/5
The tool set provides comprehensive coverage for patent search and retrieval, including query building, multiple search types, and detailed information access. A minor gap is the lack of explicit update or delete operations, but this is reasonable given the server's focus on querying a static patent database, and agents can work effectively with the provided tools.
Average 3.3/5 across 6 of 6 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves detailed bibliographic information, implying a read-only operation, but doesn't disclose other behavioral traits such as authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and usage. There is no wasted language, and every sentence contributes essential information without redundancy.
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's complexity (a single-parameter lookup tool), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the return values include (e.g., fields in the bibliographic information), error conditions, or other contextual details needed for effective use. The description should provide more context to compensate for the absence of structured data.
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 minimal meaning beyond the input schema. It mentions that the parameter 'applicationNumber' is used to fetch detailed bibliographic information for a single patent, but the schema already has 100% coverage with a clear description and example. Since schema_description_coverage is high, the baseline is 3, and the description doesn't significantly enhance parameter understanding beyond what the schema 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's purpose: 'KIPRIS Plus 서지정보 상세 조회' (KIPRIS Plus bibliographic detail lookup) and specifies it retrieves detailed bibliographic information for a single patent using an application number. It distinguishes from siblings by focusing on detailed lookup rather than search or query-building operations. However, it doesn't explicitly contrast with specific sibling tools like 'kipris_item_search' which might also retrieve patent details.
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 minimal usage guidance: it indicates the tool is for retrieving detailed bibliographic information by application number. However, it offers no explicit guidance on when to use this tool versus alternatives (e.g., when to use 'kipris_search' for broader searches or 'kipris_item_search' for other item types), no prerequisites, and no exclusions. Usage is implied but not clearly articulated.
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 the search scope but lacks critical details: whether this is a read-only operation, any rate limits, authentication requirements, or what the output format looks like (since no output schema exists). For a search tool with 4 parameters, this is insufficient behavioral context.
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, consisting of two sentences that directly state the tool's function and scope. There's no wasted text, though it could be slightly more structured (e.g., separating scope from action).
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 complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral details (e.g., read-only status, output format), usage guidelines relative to siblings, and full parameter context. For a search tool in a suite with multiple similar tools, this leaves significant gaps for an agent.
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 minimal parameter semantics beyond the schema. It mentions '키워드를 입력하면' (if you input a keyword), which aligns with the 'keyword' parameter, but doesn't explain the other parameters (numOfRows, pageNo, sortSpec). With 75% schema description coverage (3 of 4 parameters have descriptions), the baseline is 3, as the schema does most of the work, but the description doesn't compensate for the remaining gap.
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: 'KIPRIS Plus 자유 검색' (free search) that searches across '발명명칭·요약·청구범위 전체' (invention name, abstract, and claims). It specifies the verb (search) and resource (patent documents), but doesn't explicitly differentiate from sibling tools like 'kipris_search' or 'kipris_item_search', which likely have different search scopes or methods.
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 (e.g., kipris_search, kipris_item_search). It mentions the search scope (invention name, abstract, claims) but doesn't specify alternatives or exclusions, leaving the agent to guess based on tool names alone.
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 burden for behavioral disclosure. It mentions the API used and that parameters are sent using '공식 파라미터명' (official parameter names), but doesn't describe what the tool returns, pagination behavior, rate limits, authentication requirements, or error handling. For a search tool with 15 parameters, this is insufficient.
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 concise with two sentences that convey the core functionality and technical implementation. It's front-loaded with the main purpose and efficiently mentions the API usage without unnecessary elaboration.
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?
For a complex search tool with 15 parameters and no output schema, the description is incomplete. It doesn't explain what results are returned, their format, or how to interpret them. With no annotations and no output schema, the agent lacks crucial information about the tool's behavior and output.
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 schema already documents all 15 parameters thoroughly with examples and defaults. The description adds minimal value by mentioning that parameters correspond to '공식 파라미터명' (official parameter names) like 'inventionTitle, applicant, inventors', but doesn't provide additional semantic context beyond what's in the 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 performs 'KIPRIS Plus 항목별 검색' (item-by-item search) and specifies it searches by individual fields like invention title, applicant, inventor, IPC, and dates. It mentions using the 'getAdvancedSearch API' which provides technical context. However, it doesn't explicitly differentiate from sibling tools like 'kipris_free_search' or 'kipris_search'.
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 like 'kipris_free_search' or 'kipris_search'. It mentions the API used ('getAdvancedSearch API') but doesn't explain what makes this tool distinct from its siblings or when it's the appropriate choice.
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?
No annotations are provided, so the description carries the full burden. It describes the conversion behavior but lacks details on potential limitations, error handling, or output format. For a tool with no annotation coverage, this is a significant gap in behavioral 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 appropriately sized and front-loaded, consisting of two concise sentences that directly explain the tool's function and usage without any wasted words. Every sentence earns its place by providing essential information.
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 complexity (simple conversion with one parameter), no annotations, and no output schema, the description is adequate but incomplete. It explains the purpose and basic usage but lacks details on output format or behavioral traits, which are needed for full contextual understanding.
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 schema description coverage is 100%, so the schema already documents the single parameter 'intent' with examples. The description adds minimal value by restating '자연어 검색 의도' but doesn't provide additional syntax or format details beyond what the schema provides, meeting the baseline for high coverage.
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: converting natural language search intent into KIPRIS search queries. It specifies the input ('자연어 검색 의도') and output ('KIPRIS 검색식'), though it doesn't explicitly differentiate from all sibling tools like kipris_free_search or kipris_search beyond mentioning the latter as the next step.
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 clear context on when to use this tool: for converting natural language intent into a structured query, and it explicitly states to pass the result to kipris_search for actual searching. However, it doesn't mention when not to use it or alternatives among siblings like kipris_free_search.
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?
With no annotations provided, the description carries full burden. It mentions the tool supports patent/utility inclusion filters and administrative status filters, which adds useful context. However, it doesn't disclose important behavioral traits like rate limits, authentication requirements, pagination behavior, or what the response format looks like.
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 concise with two sentences that efficiently convey the tool's purpose and key features. It's front-loaded with the core functionality and follows with supporting details about the endpoint and filters.
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 search tool with 8 parameters, 100% schema coverage, but no annotations or output schema, the description provides adequate context about what the tool does and its endpoint distinction. However, it lacks information about response format, error handling, and other behavioral aspects that would be helpful for an AI agent.
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 schema already documents all 8 parameters thoroughly. The description mentions patent/utility inclusion and administrative status filters, which aligns with parameters but doesn't add significant meaning beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.
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 patents/utility models using CPC classification codes, with specific mention of patent/utility inclusion and administrative status filters. It distinguishes from 'getAdvancedSearch' by mentioning a dedicated endpoint, but doesn't explicitly differentiate from sibling tools like 'kipris_search' or 'kipris_free_search'.
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 clear context about when to use this tool by mentioning it uses a dedicated endpoint separate from 'getAdvancedSearch' and supports specific filters. However, it doesn't explicitly state when NOT to use it or provide alternatives among the sibling tools listed.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the scope of the search (full text across multiple fields) and mentions the sibling alternative, but doesn't describe what the tool returns, any rate limits, authentication requirements, or potential side effects. The description adds some context but lacks comprehensive behavioral information.
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 (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first states what the tool does, the second provides clear usage guidance. There's zero wasted text or redundancy.
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 5 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks information about what the tool returns. For a search tool with no output schema, the description should ideally mention the type of results returned (e.g., patent documents, metadata). The current description is complete for basic usage but has gaps in behavioral context.
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 schema already fully documents all 5 parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions '자유 키워드' (free keywords) which aligns with the query parameter, but provides no additional syntax, format, or usage details. Baseline 3 is appropriate when 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 specific action ('키워드 검색' - keyword search) and resource ('특허/실용신안 전문' - patent/utility model full text), distinguishing it from the sibling tool kipris_item_search for field-specific searches. It explicitly mentions searching across multiple text fields (invention name, summary, claims).
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 guidance on when to use this tool ('자유 키워드로' - with free keywords) versus when to use the alternative ('특정 필드로 좁혀 검색하려면 kipris_item_search를 사용하세요' - use kipris_item_search for narrowing by specific fields). This clearly distinguishes it from the sibling tool.
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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