OP.GG Esports MCP 서버는 OP.GG Esports 데이터를 AI 에이전트 및 플랫폼과 원활하게 연결하는 모델 컨텍스트 프로토콜(MCP) 구현입니다. 이 서버를 통해 AI 에이전트는 함수 호출을 통해 예정된 리그 오브 레전드 경기 일정 및 정보를 검색할 수 있습니다.
이 MCP 서버는 표준화된 인터페이스를 통해 AI 에이전트에게 OP.GG Esports 데이터에 대한 접근 권한을 제공합니다. TypeScript와 Node.js 기반으로 구축된 이 서버는 OP.GG Esports GraphQL API에 직접 연결하여 AI 모델과 에이전트 프레임워크에서 쉽게 사용할 수 있는 방식으로 데이터를 포맷합니다.
Related MCP server: OP.GG MCP Server
OP.GG Esports MCP 서버는 현재 다음 도구를 지원합니다.
get-lol-matches : OP.GG Esports에서 다가올 리그 오브 레전드 경기 일정을 가져오고 포맷합니다.
경기 이름, 리그, 상태, 점수, 예정된 시간 및 경기에 대한 직접 링크를 반환합니다.
AI 소비를 위해 데이터를 깔끔하고 체계적인 형식으로 포맷합니다.
Smithery를 통해 Claude Desktop에 OP.GG Esports MCP를 자동으로 설치하는 방법:
지엑스피1
# Install dependencies
pnpm install
# Build the project
pnpm build# Start the MCP server on stdio
pnpm start# Start using Node.js
node dist/index.js# Run directly with npx
npx -y @opgg/esports-mcpMCP 구성(예: Windsurf의 mcp_config.json)에 이 서버를 추가하려면 다음 항목을 추가하세요.
{
"mcpServers": {
"opgg-esports": {
"command": "node",
"args": ["/path/to/esports-mcp/dist/index.js"]
}
}
}또는 게시된 npm 패키지를 사용할 수 있습니다.
{
"mcpServers": {
"opgg-esports": {
"command": "npx",
"args": ["-y", "@opgg/esports-mcp"]
}
}
}OP.GG Esports MCP 서버는 모든 MCP 호환 클라이언트와 함께 사용할 수 있습니다. 다음은 몇 가지 예입니다.
{ "type": "list_tools" }응답:
{
"tools": [
{
"name": "get-lol-matches",
"description": "Get upcoming LoL match schedules from OP.GG Esports"
}
]
}{
"type": "tool_call",
"tool_call": {
"name": "get-lol-matches"
}
}응답:
{
"content": [
{
"type": "text",
"text": "Upcoming match schedules:\n\nMatch: Team A vs Team B\nLeague: LCK\nStatus: SCHEDULED\nScore: 0 - 0\nScheduled at: 4/6/2025, 7:00:00 PM\nDetails: https://esports.op.gg/matches/12345\n---\n..."
}
]
}이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Get upcoming LoL match schedules from OP.GG Esports
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 retrieving data ('Get') but lacks details on rate limits, authentication needs, error handling, or response format. This leaves significant gaps in understanding how the tool behaves operationally.
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 a single, efficient sentence that directly states the tool's function without any redundant or unnecessary information. It is perfectly front-loaded and wastes no words, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description adequately covers the basic purpose. However, it lacks details on behavioral aspects like data freshness, pagination, or error cases, which would be helpful given the absence of annotations and output schema.
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 tool has zero parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose, which aligns well with the empty input schema.
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 action ('Get') and resource ('upcoming LoL match schedules from OP.GG Esports'), providing a specific purpose. It distinguishes the tool by specifying the data source (OP.GG Esports) and content type (match schedules). However, without sibling tools, full differentiation isn't demonstrated, preventing a perfect score.
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, prerequisites, or contextual constraints. It merely states what the tool does without indicating appropriate scenarios or limitations, leaving usage entirely implicit.
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. Dates show when Glama detected each change.
get-lol-matchesWith only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the sole operation available.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'get-lol-matches' follows a clear verb-noun pattern.
One tool is too few for a server named 'opgg-esports', which suggests a broader domain of esports data. This minimal set severely limits functionality and feels incomplete for the apparent scope.
The tool set is severely incomplete for an esports data server. It only provides match schedules, with no coverage for other essential data like results, team info, player stats, or tournament details, leaving significant gaps.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Football fixtures, standings, and odds intelligence for AI agents.
Real-time data API for AI Agents: stocks, weather, forex, logistics, search, scrape, news, IP.
Teamfight Tactics data & AI coaching for Claude and ChatGPT — 19 tools, built-in Riot key.
Official-source financial data for AI agents: Korea, US, Taiwan, Japan, Europe. 37 tools, free tier.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/opgginc/esports-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
