Sports MCP Server

npm install apify-client

pip install apify-client

# 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 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_scores` returns 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:

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?fpr=2ayu9b

### 🔗 Related NexGenData Actors

- [NBA Scoreboard Scraper](https://apify.com/nexgendata/nba-scoreboard?fpr=2ayu9b) — direct NBA scoreboard scraper (no MCP wrapper)
- [NFL Scoreboard Scraper](https://apify.com/nexgendata/nfl-scoreboard?fpr=2ayu9b) — direct NFL scoreboard scraper
- [NHL Scoreboard Scraper](https://apify.com/nexgendata/nhl-scoreboard?fpr=2ayu9b) — direct NHL scoreboard scraper
- [News MCP Server](https://apify.com/nexgendata/news-mcp-server?fpr=2ayu9b) — news + media monitoring for AI agents
- [Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server?fpr=2ayu9b) — finance + market data for AI agents

# Actor input Schema

## Actor input object example

```json
{}

import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the ' ' with your token
const client = new ApifyClient({
 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

from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace ' ' with your token.
client = ApifyClient(" ")

# 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

echo '{}' |
apify call nexgendata/sports-mcp-server --silent --output-dataset

{
 "openapi": "3.0.1",
 "info": {
 "title": "🏈 Sports MCP — Live NBA, NFL & NHL Scores",
 "description": "MCP server giving AI agents live NBA, NFL, and NHL scoreboards (scores, teams, game status) as clean JSON. 4 tools for Claude Desktop, Cursor, and other MCP clients. Scoreboards only; pay-per-event, no subscription.",
 "version": "0.0",
 "x-build-id": "1QMNmtehNwpy4b6NQ"
 },
 "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
 }
 }
 }
 }
 }
 }
 }
 }
 }
}

{
 "mcpServers": {
 "apify": {
 "command": "npx",
 "args": [
 "mcp-remote",
 "https://mcp.apify.com/?tools=nexgendata/sports-mcp-server",
 "--header",
 "Authorization: Bearer "
 ]
 }
 }
}