Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Korean Patent MCP Search for Samsung Electronics' registered patents"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
νκ΅μ΄ λ¬Έμλ μλμ μμ΅λλ€ / Korean documentation below
MCP (Model Context Protocol) server for KIPRIS (Korean Intellectual Property Rights Information Service) API.
This MCP server enables AI assistants (Claude Desktop, Cursor, Windsurf, etc.) to search and analyze Korean patents through natural language queries. It connects to the official KIPRIS Plus Open API provided by the Korean Intellectual Property Office (KIPO).
Tool | Description |
| Search patents by applicant name |
| Get detailed patent information by application number |
| Find patents that cite a specific patent |
# Install via Smithery (recommended)
npx -y @smithery/cli install korean-patent-mcp --client claude
# Or install via uv
uv pip install git+https://github.com/Tech-curator/korean-patent-mcp.gitPython 3.10+
KIPRIS Plus Open API Key (Get your key here)
Set the KIPRIS_API_KEY environment variable:
export KIPRIS_API_KEY="your_api_key_here"Or add to your MCP client configuration:
{
"mcpServers": {
"korean-patent": {
"command": "uv",
"args": ["run", "korean-patent-mcp"],
"env": {
"KIPRIS_API_KEY": "your_api_key_here"
}
}
}
}"Search for Samsung Electronics' registered patents"
"Get details for patent application number 1020200123456"
"Find patents that cite application 1020180056789"
Related MCP server: .KCI MCP Server (νκ΅νμ μ§μΈμ©μμΈ)
νκ΅ νΉνμ 보 κ²μμλΉμ€(KIPRIS) APIλ₯Ό μν MCP(Model Context Protocol) μλ²μ λλ€.
Claude Desktop, Cursor, Windsurf λλ λ€λ₯Έ MCP ν΄λΌμ΄μΈνΈμ μ°λνμ¬ μμ°μ΄λ‘ νκ΅ νΉνλ₯Ό κ²μνκ³ λΆμν μ μμ΅λλ€.
Tool | μ€λͺ |
| μΆμμΈλͺ μΌλ‘ νΉν κ²μ |
| μΆμλ²νΈλ‘ νΉν μμΈ μ 보 μ‘°ν |
| νΉμ νΉνλ₯Ό μΈμ©ν νν νΉν μ‘°ν |
kipris_get_cpc_codes - CPC λΆλ₯ μ½λ μ‘°ν
kipris_get_inventors - λ°λͺ
μ μ 보 μ‘°ν
kipris_check_rejection - κ±°μ μ¬λΆ νμΈ
kipris_analyze_rejection_reason - κ±°μ μ¬μ λΆμ
# Smithery CLI μ€μΉ (μ²μ ν λ²λ§)
npm install -g @smithery/cli
# Korean Patent MCP μλ² μ€μΉ
smithery install korean-patent-mcp --client claude# μ μ₯μ ν΄λ‘
git clone https://github.com/khreat/korean-patent-mcp.git
cd korean-patent-mcp
# uvλ‘ μ€μΉ (κΆμ₯)
uv pip install -e .
# λλ pipμΌλ‘ μ€μΉ
pip install -e .Python 3.10+
KIPRIS Plus Open API ν€ (λ°κΈ μ¬μ΄νΈ)
.env νμΌμ μμ±νκ³ API ν€λ₯Ό μ€μ ν©λλ€:
echo "KIPRIS_API_KEY=your_api_key_here" > .envλλ νκ²½λ³μλ‘ μ€μ :
export KIPRIS_API_KEY="your_api_key_here"~/Library/Application Support/Claude/claude_desktop_config.json (macOS) λλ
%APPDATA%\Claude\claude_desktop_config.json (Windows) νμΌμ νΈμ§ν©λλ€:
{
"mcpServers": {
"korean-patent": {
"command": "uv",
"args": ["run", "korean-patent-mcp"],
"env": {
"KIPRIS_API_KEY": "your_api_key_here"
}
}
}
}MCP μ€μ μμ λ€μμ μΆκ°ν©λλ€:
{
"korean-patent": {
"command": "uv",
"args": ["run", "korean-patent-mcp"],
"env": {
"KIPRIS_API_KEY": "your_api_key_here"
}
}
}Claude Desktopμμ λ€μκ³Ό κ°μ΄ μ§λ¬Έν μ μμ΅λλ€:
μΌμ±μ μκ° μΆμν νΉν μ€ λ±λ‘λ κ²λ€μ 보μ¬μ€μΆ©λΆλνκ΅ μ°ννλ ₯λ¨μ μ΅κ·Ό νΉνλ₯Ό κ²μν΄μ€μΆμλ²νΈ 1020200123456μ νΉν μμΈ μ 보λ₯Ό μλ €μ€μΆμλ²νΈ 1020180056789λ₯Ό μΈμ©ν νΉνλ€μ μ°Ύμμ€npx @modelcontextprotocol/inspector uv run korean-patent-mcpsmithery devkorean-patent-mcp/
βββ pyproject.toml # ν¨ν€μ§ μ€μ (uv/pip νΈν)
βββ smithery.yaml # Smithery λ°°ν¬ μ€μ
βββ README.md
βββ .env.example
βββ src/
βββ korean_patent_mcp/
βββ __init__.py
βββ server.py # MCP μλ² & Tool μ μ
βββ kipris_api.py # KIPRIS API ν΄λΌμ΄μΈνΈλͺ¨λ Toolμ response_format νλΌλ―Έν°λ₯Ό μ§μν©λλ€:
markdown (κΈ°λ³Έκ°): μ¬λμ΄ μ½κΈ° μ’μ νμ
json: νλ‘κ·Έλλ° μ²λ¦¬μ μ ν©ν ꡬ쑰νλ νμ
KIPRIS APIλ νΈμΆ μ νμ΄ μμ μ μμ΅λλ€
λλ κ²μ μ νμ΄μ§λ€μ΄μ μ νμ©νμΈμ
API ν€λ μ λ κ³΅κ° μ μ₯μμ 컀λ°νμ§ λ§μΈμ
MIT License
λ²κ·Έ 리ν¬νΈ, κΈ°λ₯ μ μ, PR λͺ¨λ νμν©λλ€!
Tech Curator: https://techcurator.kr
DiME: https://www.dime.kr
E-mail: jkh25@techcurator.kr / ceo@techcurator.kr
Made with β€οΈ for Korean patent research
νΉμ νΉνλ₯Ό μΈμ©ν νν νΉνλ€μ μ‘°νν©λλ€.
Args:
application_number: κΈ°μ€ νΉνμ μΆμλ²νΈ (νμ)
response_format: μλ΅ νμ ('markdown' λλ 'json')
| Name | Required | Description | Default |
|---|---|---|---|
| application_number | Yes | ||
| response_format | No | markdown |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 (μ‘°ν), 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.
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.
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.
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 'κΈ°μ€ νΉν' (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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'νΉμ νΉνλ₯Ό μΈμ©ν νν νΉνλ€μ μ‘°νν©λλ€' (Retrieve patents that cite a specific patent). It specifies the verb (μ‘°ν/retrieve) and resource (νν νΉνλ€/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.
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.
μΆμλ²νΈλ‘ νΉνμ μμΈ μ 보λ₯Ό μ‘°νν©λλ€.
Args:
application_number: μΆμλ²νΈ (νμ, μ: '1020200123456')
response_format: μλ΅ νμ ('markdown' λλ 'json')
| Name | Required | Description | Default |
|---|---|---|---|
| application_number | Yes | ||
| response_format | No | markdown |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 ('μ‘°νν©λλ€'), 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.
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.
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.
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 'μΆμλ²νΈ (νμ, μ: '1020200123456')' (application number, required, example) and 'response_format' as 'μλ΅ νμ ('markdown' λλ '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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'μΆμλ²νΈλ‘ νΉνμ μμΈ μ 보λ₯Ό μ‘°νν©λλ€' (Retrieve detailed information of a patent by application number). It specifies the verb 'μ‘°νν©λλ€' (retrieve) and resource 'νΉνμ μμΈ μ 보' (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.
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.
μΆμμΈλͺ μΌλ‘ νκ΅ νΉνλ₯Ό κ²μν©λλ€.
Args:
applicant_name: μΆμμΈλͺ
(νμ, μ: 'μΌμ±μ μ', 'μΉ΄μΉ΄μ€λ±
ν¬')
page: νμ΄μ§ λ²νΈ (κΈ°λ³Έκ°: 1)
page_size: νμ΄μ§λΉ κ²°κ³Ό μ (κΈ°λ³Έκ°: 20, μ΅λ: 100)
status: μν νν° ('A': 곡κ°, 'R': λ±λ‘, 'J': κ±°μ , None: μ 체)
response_format: μλ΅ νμ ('markdown' λλ 'json')
| Name | Required | Description | Default |
|---|---|---|---|
| applicant_name | Yes | ||
| page | No | ||
| page_size | No | ||
| status | No | ||
| response_format | No | markdown |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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., 'μΌμ±μ μ' for applicant_name), default values, and constraints (e.g., 'μ΅λ: 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'μΆμμΈλͺ μΌλ‘ νκ΅ νΉνλ₯Ό κ²μν©λλ€' (Search Korean patents by applicant name). It specifies the verb ('κ²μν©λλ€' - search) and resource ('νκ΅ νΉν' - 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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
kipris_get_citing_patentskipris_get_patent_detailkipris_search_patentsScored across 3 tools
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.
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.
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.
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.
AI-powered patent intelligence for search & analysis
Global patent search, briefs, similarity, citations and landscape stats. Strong China coverage.
Korean patents by meaning, not keywords: describe an invention in plain words or by drawing.
Patent search, USPTO data, patent landscape & pgvector prior-art search for agents.
