OP.GG Esports MCPサーバーは、OP.GG EsportsデータをAIエージェントおよびプラットフォームにシームレスに接続するモデルコンテキストプロトコル（MCP）実装です。このサーバーにより、AIエージェントは関数呼び出しを通じて、今後のリーグ・オブ・レジェンドの試合スケジュールや情報を取得できるようになります。
このMCPサーバーは、AIエージェントが標準化されたインターフェースを介してOP.GG Esportsデータにアクセスできるようにします。TypeScriptとNode.jsで構築されており、OP.GG Esports GraphQL APIに直接接続し、AIモデルやエージェントフレームワークで簡単に利用できる形式でデータをフォーマットします。
Related MCP server: OP.GG MCP Server
OP.GG Esports MCP サーバーは現在、次のツールをサポートしています。
get-lol-matches : OP.GG Esports から今後の League of Legends の試合スケジュールを取得してフォーマットします
試合名、リーグ、ステータス、スコア、予定時間、試合への直接リンクを返します。
AI で使用できるように、データをクリーンで構造化された形式でフォーマットします
Smithery経由で Claude Desktop に OP.GG Esports MCP を自動的にインストールするには:
npx -y @smithery/cli install @opgginc/esports-mcp --client claude# 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-mcpこのサーバーを MCP 構成 (例: Windsurf の mcp_config.json) に追加するには、次のエントリを追加します。
{
"mcpServers": {
"opgg-esports": {
"command": "node",
"args": ["/path/to/esports-mcp/dist/index.js"]
}
}
}あるいは、公開されている場合は npm パッケージを使用することもできます。
{
"mcpServers": {
"opgg-esports": {
"command": "npx",
"args": ["-y", "@opgg/esports-mcp"]
}
}
}OP.GG Esports MCPサーバーは、MCP対応のクライアントであればどれでもご利用いただけます。以下に例をいくつかご紹介します。
{ "type": "list_tools" }応答：
{
"tools": [
{
"name": "get-lol-matches",
"description": "Get upcoming LoL match schedules from OP.GG Esports"
}
]
}{
"type": "tool_call",
"tool_call": {
"name": "get-lol-matches"
}
}応答：
{
"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..."
}
]
}このプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細については LICENSE ファイルを参照してください。
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
