Sports MCP Server
NBA、NFL、NHLのライブスポーツスコアと統計情報
ドキュメント
Sports Scores MCP — NBA, NFL & NHL for AI Agents (nexgendata/sports-mcp-server) Actor
MCP server exposing NBA, NFL, NHL and combined sports scores as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK to live game scores.
- URL: https://apify.com/nexgendata/sports-mcp-server.md
- Developed by: NexGenData (community)
- Categories: AI, Social media, MCP servers
- Stats: 14 total users, 5 monthly users, 100.0% runs succeeded, 0 bookmarks
- User rating: No ratings yet
Pricing
from $10.00 / 1,000 results
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A".
How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official JavaScript/TypeScript client:
npm install apify-client
In Python projects, use official Python client library:
pip install apify-client
In shell scripts, use Apify CLI:
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Sports Scores MCP
A Model Context Protocol server that gives AI agents live sports scores — NBA, NFL, NHL and combined — as callable tools. For sports assistants, bots and dashboards.
### 🛠 Tools (4)
- `get_all_scores` — Scores across all supported leagues.
- `get_nba_scores` — NBA game scores.
- `get_nfl_scores` — NFL game scores.
- `get_nhl_scores` — NHL game scores.
### 🔌 Connect (Claude Desktop / Cursor / n8n / OpenAI Agents SDK)
Add this MCP server to your client config:
```json
{
"mcpServers": {
"sports": {
"url": "https://nexgendata--sports-mcp-server.apify.actor/mcp"
}
}
}
Sample agent prompt:
Get today's NBA and NHL scores.
Pricing: $0.02 per tool call (Pay-Per-Event). Runs in Standby mode.
🏈 Sports MCP Server — Live NBA, NFL & NHL Scores for Claude / ChatGPT
Connect AI agents to live US sports scoreboards through the Model Context Protocol (MCP) — NBA, NFL, and NHL game scores in clean JSON tuned for LLM function-calling. A lightweight, pay-per-use alternative to monthly sports-data subscriptions for agents that just need current scores.
What You Get
This MCP server exposes 4 tools to your AI agent. Each returns the current scoreboard for its league — teams, scores, and game status (live / final / scheduled). The tools take no parameters; they return the current slate.
get_nba_scores— current NBA scoreboard (teams, scores, game status, tip-off times)get_nfl_scores— current NFL scoreboard (teams, scores, game status, kickoff times)get_nhl_scores— current NHL scoreboard (teams, scores, game status, start times)get_all_scores— NBA + NFL + NHL scoreboards in a single call, ideal for a daily sports briefing
All responses are structured JSON for LLM tool use — no HTML scraping required.
Use Cases
- Fan-engagement chatbots — Discord / Slack bots answer "what's the Lakers score?" without dev overhead
- Daily sports briefings —
get_all_scoresreturns every NBA/NFL/NHL game in one call - Sports media dashboards — auto-generate game-recap copy once a game goes final
- Twitter / X bots — post score updates from structured data instead of scraping
Quick Start
Wire this MCP server into an MCP-compatible client (Claude Desktop, Cursor, Windsurf, Cline) by pointing your config at this actor's MCP endpoint:
https://nexgendata--sports-mcp-server.apify.actor/mcp
Then ask: "What NBA games are on today and what are the scores?"
Pricing
This actor uses Apify pay-per-event pricing — you are charged per successful tool call, with no monthly subscription.
FAQ
Q: Is this an official ESPN / NBA / NFL feed? No. The server aggregates publicly available league scoreboards.
Q: Which leagues are supported? NBA, NFL, and NHL scoreboards. MLB, soccer, and other leagues are not currently supported.
Q: Can I query a specific date or a past game? No. The tools return the current scoreboard and take no parameters.
Q: Does it provide standings, player stats, schedules, or play-by-play? No. This server returns scoreboards only.
Q: Can the AI agent call this from Cursor / Cline / Claude Desktop? Yes — any MCP-compatible client works. Point your config at this actor's MCP endpoint.
About NexGenData
NexGenData publishes a catalog of Apify actors and a family of MCP servers for AI agent workflows. Browse the full catalog at https://apify.com/nexgendata
🔗 Related NexGenData Actors
- NBA Scoreboard Scraper — direct NBA scoreboard scraper (no MCP wrapper)
- NFL Scoreboard Scraper — direct NFL scoreboard scraper
- NHL Scoreboard Scraper — direct NHL scoreboard scraper
- News MCP Server — news + media monitoring for AI agents
- Finance MCP Server — finance + market data for AI agents
Actor input Schema
Actor input object example
{}
API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
JavaScript example
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
token: '<YOUR_API_TOKEN>',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("nexgendata/sports-mcp-server").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
Python example
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("nexgendata/sports-mcp-server").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
CLI example
echo '{}' |
apify call nexgendata/sports-mcp-server --silent --output-dataset
MCP server setup
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=nexgendata/sports-mcp-server",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}
OpenAPI specification
{
"openapi": "3.0.1",
"info": {
"title": "Sports Scores MCP — NBA, NFL & NHL for AI Agents",
"description": "MCP server exposing NBA, NFL, NHL and combined sports scores as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK to live game scores.",
"version": "0.0",
"x-build-id": "13y93DcKxpaQdLNuM"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/nexgendata~sports-mcp-server/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-nexgendata-sports-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/nexgendata~sports-mcp-server/runs": {
"post": {
"operationId": "runs-sync-nexgendata-sports-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/nexgendata~sports-mcp-server/run-sync": {
"post": {
"operationId": "run-sync-nexgendata-sports-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}