Click on "Install 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., "@AgentSkills MCP Server Search for the latest news about Samsung Electronics"
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.
MCP (Model Context Protocol) server for AgentSkills API. Gives Claude, Cursor, and other AI tools access to web search, code review, and Korean business data.
Sign up for free at agentskills.io — no credit card required, 100 free calls/month.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"agentskills": {
"command": "npx",
"args": ["-y", "@gpters/agentskills-mcp"],
"env": {
"AGENTSKILLS_API_KEY": "sk_your_key_here"
}
}
}
}Restart Claude Desktop and you're done.
claude mcp add agentskills -- npx -y @gpters/agentskills-mcpThen set your API key:
export AGENTSKILLS_API_KEY=sk_your_key_hereOr add it to your shell profile (~/.zshrc, ~/.bashrc).
Open Settings → MCP and add:
{
"agentskills": {
"command": "npx",
"args": ["-y", "@gpters/agentskills-mcp"],
"env": {
"AGENTSKILLS_API_KEY": "sk_your_key_here"
}
}
}Related MCP server: Korean Company Information MCP Server
web_searchReal-time web search with structured results and AI summary.
Parameters:
query (required) — search query
maxResults — number of results (default: 5)
language — "ko" or "en" (default: "ko")
Example:
"Search for the latest news about Samsung Electronics"
code_reviewAnalyzes code for bugs, security issues, and improvement suggestions.
Parameters:
code (required) — source code (max 50,000 chars)
language — programming language (optional, auto-detected)
focus — "security", "performance", "quality", or "all" (default)
Example:
"Review this TypeScript file for security issues"
business_dataKorean company information, news, and financial data.
Parameters:
type (required) — "company-info", "news", or "financial"
companyName — company name in Korean or English
businessNumber — Korean business registration number (사업자등록번호)
Example:
"Look up company info for 삼성전자"
Plan | Monthly calls | Price |
Free | 100 | ₩0 |
Pro | 10,000 | ₩49,000/mo |
Enterprise | Unlimited | Contact us |
MIT
Look up Korean business data including company information, news, and financial data. Useful for researching Korean companies by name or business registration number.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Type of data to retrieve: 'company-info', 'news', or 'financial' | |
| companyName | No | Company name in Korean or English (e.g., '삼성전자', 'Samsung Electronics') | |
| businessNumber | No | Korean business registration number (사업자등록번호). Optional alternative to companyName. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It identifies the operation as 'look up' (read-only implication) and data types retrieved, but omits critical operational details: error behavior when companies aren't found, data freshness/recency, rate limits, or whether results are structured vs. plain text.
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?
Two sentences efficiently structured: capability declaration followed by use case. No redundancy or filler content, though the second sentence partially overlaps with the schema's explicit mentioning of identification methods.
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?
Adequate for a 3-parameter lookup tool with complete schema coverage. However, lacking both annotations and output schema, the description should ideally disclose return value structure or empty-result behavior. As written, it leaves the agent uncertain about response format.
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?
Schema description coverage is 100%, so the baseline applies. The description mirrors the schema content (mapping enum values to data types and noting the identification alternatives) but does not add syntactic constraints, validation rules, or interaction logic beyond what the schema already documents.
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?
Clear verb 'Look up' and specific resource 'Korean business data' with enumerated subtypes (company info, news, financial). However, it lacks explicit differentiation from sibling 'web_search', which could also retrieve company information, leaving the agent to infer the specialization from 'Korean' context.
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?
States the tool is 'Useful for researching Korean companies' and identifies the two lookup methods (name or registration number), providing implied context. However, it lacks explicit 'when-not-to-use' guidance or comparison to 'web_search' alternative, leaving ambiguity about when this specialized tool is preferred over general search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze code for issues, security vulnerabilities, and improvement suggestions. Returns a score, summary, list of issues with severity, and positive observations.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The source code to review (max 50,000 characters) | |
| language | No | Programming language of the code (e.g., 'typescript', 'python', 'go'). Optional. | |
| focus | No | Review focus area: 'security', 'performance', 'quality', or 'all' (default) | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It compensates for missing output schema by describing return values (score, summary, issues, observations). However, lacks operational details like side effects, code privacy/storage policies, or rate limits expected for a code submission tool.
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?
Two well-structured sentences: first states purpose, second describes output. No redundancy or waste. Front-loaded with the most critical information (what it does) before return value details.
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?
Good coverage given constraints: describes output shape to compensate for missing output schema, and all parameters are well-defined in schema. Could improve by noting privacy considerations when submitting code or supported language nuances, but functionally complete for invocation.
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?
Schema coverage is 100% with full descriptions for all 3 parameters including enums and defaults. Description doesn't add parameter-specific semantics beyond the schema, but the comprehensive schema makes additional description unnecessary. Baseline 3 appropriate for high-coverage schemas.
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?
Clear specific verb ('Analyze') and resource ('code') with scope covering issues, security, and improvements. Clearly distinguishes from siblings 'business_data' and 'web_search' by domain (code analysis vs. business data/web search).
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?
Usage is implied by the clear domain description (code analysis), but lacks explicit guidelines on when to use vs. siblings or prerequisites like 'use when you need static analysis' or 'do not use for runtime debugging'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a real-time web search and get structured results with a summary. Returns top results with title, URL, snippet, and an AI-generated summary.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query | |
| maxResults | No | Maximum number of results to return (default: 5) | |
| language | No | Language for results: 'ko' (Korean) or 'en' (English). Default: ko | ko |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses 'real-time' nature (fresh data), result structure ('title, URL, snippet'), and AI-generated summary feature. Missing rate limits, auth requirements, or caching behavior, but covers primary behavioral traits well.
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?
Two efficiently structured sentences with zero waste. First sentence front-loads action and value proposition ('Perform... and get structured results'). Second sentence details output format. Every word earns its place.
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?
No output schema exists, but description compensates by detailing return structure (title, URL, snippet, AI summary). 100% input schema coverage reduces documentation burden. Lacks error handling or rate limit disclosure, but complete for a standard search 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?
Schema coverage is 100% with clear descriptions for all 3 parameters (query, maxResults, language including defaults). Description adds no parameter-specific semantics beyond the schema, which is acceptable given the complete schema documentation. Baseline 3 appropriate.
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?
Clear specific verb ('Perform') + resource ('web search'). Distinguishes effectively from siblings 'business_data' (internal company data) and 'code_review' (code analysis) by specifying 'web' as the domain.
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?
Usage is implied by the domain specificity ('web search' vs internal 'business' data), but lacks explicit guidance on when to choose this over 'business_data' for company information or 'code_review' for code-specific queries. No 'when not to use' or prerequisite guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: business_data focuses on Korean business information, code_review analyzes code quality and security, and web_search performs general web searches. There is no overlap in functionality or ambiguity between these tools.
The naming follows a consistent snake_case pattern (business_data, code_review, web_search), which is readable and predictable. The only minor deviation is that 'business_data' uses a compound noun while the others are more action-oriented, but this doesn't break the overall consistency.
With only 3 tools, the server feels thin for a general-purpose 'AgentSkills' server. While each tool is distinct, the scope implied by the server name suggests a broader set of agent capabilities might be expected, making this borderline appropriate.
For a server named 'AgentSkills', there are notable gaps in common agent capabilities such as file operations, data processing, or communication tools. However, the three provided tools cover useful domains (business research, code analysis, web search) without dead ends in their respective areas.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Korean fact-verification tools for AI agents: business registration, address, DART, apt prices, laws
Direct access to 40+ scraping and search tools. Extract structured data from Google (Search, Maps, Trends), Amazon, Airbnb, Social Media, and any web page directly into your AI agent.
Agent-native search engine with live web research optimized for AI agents.
Give your agent web search and authoritative datasets: S&P Global, FRED, OECD, SimilarWeb & more.
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