MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
作者による説明(英語の原文)
Local sports math, DFS settlement, bet analytics, and game-entertainment tools.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"dfs-engine": {
"command": "npx",
"args": [
"-y",
"@buzzr/mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add dfs-engine -- npx -y @buzzr/mcpプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"dfs-engine": {
"command": "npx",
"args": [
"-y",
"@buzzr/mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"dfs-engine": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@buzzr/mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント Buzzr-app で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(5.1.0)がレジストリに届いたのは4 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
MCP server exposing the @buzzr sports engines to AI agents — DFS settlement, odds math, and entertainment predictions as tools.
@buzzr/mcp is a Model Context Protocol stdio server that puts the whole @buzzr engine family in front of any MCP-capable agent (Claude Desktop, Claude Code, or your own client). All math and policy logic lives in the underlying engines — this package is a thin, schema-validated tool surface:
@buzzr/dfs-engine — pick-em settlement with versioned compatibility and custom policies@buzzr/bets-core — no-vig fair lines, parlay pricing, expected value, Kelly staking@buzzr/entertainment-engine — ML buzz-score predictions and personalized game rankingRun it directly with npx (Node 22+). Pin the version you reviewed instead of silently accepting a future latest release:
npx -y @buzzr/mcp@5.1.0
The server speaks MCP over stdio: JSON-RPC on stdin/stdout, logs on stderr. It exposes 11 tools with bounded inputs and outputs, and does not fetch live odds, box scores, operator accounts, or private user data.
The Buzzr Sports Engine listing on Smithery packages the exact published @buzzr/mcp@5.1.0 artifact as a local stdio MCPB. It is a distribution and installation surface, not a hosted HTTP service. To add it to Codex through Smithery:
npx -y smithery@1.2.0 mcp add sarveshsea/buzzr-sports-engine --client codex
Smithery writes the corresponding MCP configuration for the selected client. Use the direct npm command above when you need both the server package and its launcher configuration pinned explicitly.
Add this to claude_desktop_config.json from Settings → Developer → Edit Config, then fully quit and reopen Claude Desktop:
{
"mcpServers": { "buzzr": { "command": "npx", "args": ["-y", "@buzzr/mcp@5.1.0"] } } }
claude mcp add --transport stdio buzzr -- npx -y @buzzr/mcp@5.1.0
For a version-controlled project configuration, add this to .mcp.json, trust the project when prompted, and start a new Claude Code session:
{
"mcpServers": { "buzzr": { "command": "npx", "args": ["-y", "@buzzr/mcp@5.1.0"] } } }
Confirm it with claude mcp get buzzr; inside Claude Code, /mcp shows the connection and discovered tool count.
Add this to the repository's .cursor/mcp.json (or the equivalent user-level MCP configuration), then fully restart Cursor:
{
"mcpServers": { "buzzr": { "command": "npx", "args": ["-y", "@buzzr/mcp@5.1.0"] } } }
Open Cursor Settings → MCP to confirm buzzr connected and exposed 11 tools. See Cursor's MCP documentation if your installed Cursor version presents a different settings location.
The Codex CLI, IDE extension, and app share config.toml. The one-command user setup is:
codex mcp add buzzr -- npx -y @buzzr/mcp@5.1.0
Or add the equivalent block to ~/.codex/config.toml for all projects, or to a trusted repository's .codex/config.toml for that project only:
[mcp_servers.buzzr]
command = "npx" args = ["-y", "@buzzr/mcp@5.1.0"]
Run codex mcp get buzzr, then start a new Codex task after changing the configuration.
Use this process configuration in any client that accepts a command plus args:
{
"name": "buzzr", "transport": "stdio", "command": "npx", "args": ["-y", "@buzzr/mcp@5.1.0"], "env": {} }
Treat stdout as JSON-RPC only, read diagnostics from stderr, and close the child process's stdin during shutdown. The client must perform the MCP lifecycle below; starting npx in a terminal and seeing it remain open is normal for a stdio server waiting for a client.
The client sends initialize first. Verify the serverInfo.name field is "buzzr", the serverInfo.version field is the installed package version, and the response has a capabilities.tools object. Send notifications/initialized, then call tools/list. The server returns the 11 tools in the catalog below with their input schemas. It does not advertise data fetching, resources, or prompts.
examples/mcp-calls.json is a machine-readable JSON-RPC transcript with the initialization expectation, exact tool discovery order, and a safe list_book_policies → validate_dfs_entry → grade_dfs_entry workflow. The repository replays that workflow through a real MCP client in CI.
node --version, npm --version, and npx --version.npm view @buzzr/mcp@5.1.0 version, then run npm cache verify. If npm reports cache corruption, repair npm's cache before retrying; npm cache clean --force is a last resort because it removes the whole local cache.PATH than an interactive shell. On macOS or Linux, run command -v npx; on Windows, run where.exe npx. If the client cannot find npx, use that absolute path as command (normally npx.cmd on Windows) or launch the client from an environment where Node is on PATH.listening on stdio diagnostic to stderr and waits for JSON-RPC input.| Tool | Engine | What it does |
|---|---|---|
grade_dfs_entry | @buzzr/dfs-engine | Settle one transport-bounded 1–12-leg entry; executable built-in policies currently allow at most 6 PrizePicks or 8 Underdog legs. |
grade_dfs_entries | @buzzr/dfs-engine | Settle 1–50 entries, up to 600 total legs, with bounded concurrency and isolated failures. |
validate_dfs_entry | @buzzr/dfs-engine | Return structured engine validation issues for a candidate entry without settling it. |
list_book_policies | @buzzr/dfs-engine | List authoritative executable profile snapshots and metadata-only drafts, including status, verification, sources, and complete play types. |
fair_line | @buzzr/bets-core | Remove vig from both sides of one two-way market. |
closing_line_value | @buzzr/bets-core | Compare placed and closing prices for the same selection. |
parlay_value | @buzzr/bets-core | Price independent parlay legs, compare offered odds, and optionally calculate expected value. |
kelly_stake | @buzzr/bets-core | Calculate full and fractional Kelly stakes from a supplied win probability. |
summarize_bet_history | @buzzr/bets-core | Summarize up to 500 bets with overall and UTC-period rollups, drawdown, and streaks. |
predict_game_buzz | @buzzr/entertainment-engine | Predict one game's 1–10 entertainment score with confidence and factor detail. |
rank_games | @buzzr/entertainment-engine | Rank 1–100 candidate games for a bounded taste profile. |
Tool strings, identifiers, arrays, stdio frames, concurrent calls, and serialized results are bounded. American odds must be within [-100000, -100] or [100, 100000]. grade_dfs_entries, closing_line_value, and summarize_bet_history return string contractVersion: "1".
Operator-named policies are independent compatibility profiles, not official rules engines or evidence of affiliation:
Displayed lineup terms are authoritative. Call list_book_policies before grading, inspect verification and sources, and obtain explicit operator rulings for DNPs, reboots, ties, rescues, voids, and corrections. The tool lists future fixtures with executable: false; grading tools reject draft book IDs because drafts are metadata, not settlement implementations.
invalid_input result through an MCP client transport or a direct exported tool.handler(...) call. At most eight compact validation issues are included; raw Zod errors are never returned.tools/call request with invalid tool arguments.validate_dfs_entry intentionally accepts a bounded candidate object and returns the engine's structured validation report.isError: true results such as tool_execution_failed, entry_settlement_failed, server_busy, or result_too_large. Internal error details are not public."Did my PrizePicks slip hit?"
User: I had a $10 2-pick power play: LeBron over 25.5 points (he scored 31)
and Curry over 27.5 (he scored 33). What's the payout?
Agent calls
grade_dfs_entry:
`json
{
"entryId": "slip-1",
"bookId": "prizepicks",
"playTypeId": "power",
"stake": 10,
"displayedMultiplier": 3,
"legs": [
{ "legId": "l1", "playerName": "LeBron James", "league": "NBA", "propType": "points", "line": 25.5, "direction": "over", "actual": 31 },
{ "legId": "l2", "playerName": "Stephen Curry", "league": "NBA", "propType": "points", "line": 27.5, "direction": "over", "actual": 33 }
]
}
`
Result:
"status": "won","payout": { "total": 30, "withdrawable": 30, "bonus": 0 }—
both supplied actuals clear their lines and the submitted 3× displayed multiplier
is consistent with the selected compatibility table. Confirm the actual entry
details before tr
冒頭だけを表示しています。続きはリポジトリで読めます