# ESPN Sports MCP Server (`automation-lab/espn-sports-mcp-server`) Actor MCP-ready ESPN sports tools for live scores, schedules, standings, teams, summaries, and odds from public ESPN JSON endpoints. - **URL**: https://apify.com/automation-lab/espn-sports-mcp-server.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # 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 ## ESPN Sports MCP Server Turn ESPN's public sports JSON into MCP-ready tools and clean Apify datasets. This Actor gives AI agents, sports desks, fantasy analysts, and automation teams a hosted way to ask for live scoreboards, schedules, teams, standings, game summaries, leaders, and odds text when ESPN includes it. The Actor is HTTP-only, does not require login, and works with ESPN sport/league paths such as `football/nfl`, `basketball/nba`, `baseball/mlb`, `hockey/nhl`, `soccer/eng.1`, and `soccer/usa.1`. ### What does ESPN Sports MCP Server do? ESPN Sports MCP Server exposes a small set of reliable sports-data tools: - ๐Ÿˆ `get_scoreboard` โ€” current or dated ESPN scoreboard events - ๐Ÿ“… `get_schedule` โ€” schedule-style scoreboard ranges by date - ๐Ÿงพ `get_game_summary` โ€” leaders, odds, and summary rows for a specific ESPN event ID - ๐Ÿ† `get_standings` โ€” league standings from ESPN's public standings endpoint - ๐ŸŸ๏ธ `get_teams` โ€” team directory data with IDs, names, logos, colors, and links Each run writes normalized records to the default dataset. You can also set `includeRaw` to keep compact ESPN source fragments for advanced agent fallback logic. ### Who is it for? #### AI-agent builders Use it as a hosted ESPN MCP tool source for Claude, ChatGPT, LangChain, CrewAI, and custom agent workflows. Agents can call one tool at a time and receive structured rows instead of parsing a sports website. #### Sports media teams Create match previews, daily score digests, live-score summaries, editorial calendars, and post-game briefs from ESPN event data. #### Fantasy and betting analysts Collect game IDs, score states, standings, leaders, and ESPN odds text when present. The Actor does not place bets or provide betting advice; it simply structures ESPN public data. #### Dashboard and alerting teams Backfill schedules, monitor current scoreboards, and feed BI tools or notification systems with consistent fields. ### Why use this Actor? - โœ… MCP-first tool names for agent workflows - โœ… Clean dataset rows for exports, APIs, and automations - โœ… No browser, no login, no user account required - โœ… Supports multiple ESPN sports and leagues in one Actor - โœ… Optional raw fallback for endpoint drift - โœ… Low-cost HTTP implementation - โœ… Works from Apify API, scheduled tasks, webhooks, and MCP clients ### Supported ESPN leagues The `league` input accepts ESPN `sport/league` paths. Common examples: | Sport | ESPN path | Example use | | --- | --- | --- | | NFL | `football/nfl` | live NFL scoreboard and standings | | NBA | `basketball/nba` | NBA games, teams, standings | | MLB | `baseball/mlb` | baseball schedules and scoreboard ranges | | NHL | `hockey/nhl` | hockey games and standings | | Premier League | `soccer/eng.1` | soccer fixtures and scores | | La Liga | `soccer/esp.1` | Spanish soccer schedules | | Serie A | `soccer/ita.1` | Italian soccer schedules | | Bundesliga | `soccer/ger.1` | German soccer schedules | | Ligue 1 | `soccer/fra.1` | French soccer schedules | | MLS | `soccer/usa.1` | MLS fixtures and scores | Other ESPN public `sport/league` paths may work if ESPN exposes the same JSON structure. ### Data you can extract | Field group | Example fields | | --- | --- | | Event identity | `eventId`, `name`, `shortName`, `date`, `league` | | Game status | `status`, `statusDetail`, `winner` | | Teams and scores | `homeTeam`, `homeTeamId`, `homeScore`, `awayTeam`, `awayTeamId`, `awayScore` | | Venue and links | `venue`, `url` | | Teams | `teamId`, `teamName`, `abbreviation`, `displayName`, `location`, `logo`, `color` | | Standings | `rank`, `wins`, `losses`, `ties`, `winPercent`, `gamesBehind`, `summary` | | Summary rows | `label`, `name`, `value`, `team`, `athlete` | | Metadata | `tool`, `recordType`, `source`, `scrapedAt` | ### How much does it cost to extract ESPN sports scores and schedules? This Actor uses pay-per-event pricing: - Start fee: `$0.005` per run - Result records: tiered PPE pricing, currently `$0.000022722` per item on the BRONZE tier, with volume discounts down to `$0.00001` per item Because the Actor uses public HTTP JSON endpoints and no browser, typical runs are inexpensive. Pricing was calibrated from cloud runs with 90%+ NET margin on the realistic-scale test. ### Quick start 1. Choose a tool, for example `get_scoreboard`. 2. Choose an ESPN league path, for example `football/nfl`. 3. Optionally set `date` as `YYYYMMDD`, or `startDate` and `endDate` for a range. 4. Set `maxItems` to control output volume. 5. Run the Actor and export the default dataset. ### Example inputs #### Current NFL scoreboard ```json { "tool": "get_scoreboard", "league": "football/nfl", "maxItems": 25 } ```` #### MLB schedule range ```json { "tool": "get_schedule", "league": "baseball/mlb", "startDate": "20250601", "endDate": "20250608", "maxItems": 150 } ``` #### NBA teams ```json { "tool": "get_teams", "league": "basketball/nba", "maxItems": 50 } ``` #### NFL standings ```json { "tool": "get_standings", "league": "football/nfl", "maxItems": 100 } ``` #### Game summary ```json { "tool": "get_game_summary", "league": "football/nfl", "eventId": "401772510", "maxItems": 50 } ``` ### Output example ```json { "recordType": "event", "tool": "get_scoreboard", "league": "football/nfl", "eventId": "401772510", "name": "Dallas Cowboys at Philadelphia Eagles", "date": "2025-09-05T00:20Z", "statusDetail": "Scheduled", "venue": "Lincoln Financial Field", "homeTeam": "Philadelphia Eagles", "homeScore": 0, "awayTeam": "Dallas Cowboys", "awayScore": 0, "url": "https://www.espn.com/nfl/game/_/gameId/401772510", "scrapedAt": "2026-06-28T00:00:00.000Z" } ``` ### MCP integration Apify Actors are available through Apify's MCP server. Use the Actor as a tool by configuring the MCP URL with the Actor slug: ```text https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server ``` Claude Code CLI setup: ```bash claude mcp add apify-espn-sports https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server ``` Generic MCP JSON config: ```json { "mcpServers": { "apify-espn-sports": { "url": "https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server" } } } ``` Example prompts: - "Use ESPN Sports MCP Server to get today's NFL scoreboard and summarize games that are final." - "Fetch MLB games for the first week of June 2025 and make a content calendar." - "Get NBA teams and return team IDs for a downstream game-summary workflow." - "Check NHL standings and identify division leaders." ### Claude Desktop MCP setup Add an Apify MCP server entry that points to: ```text https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server ``` Example JSON configuration: ```json { "mcpServers": { "apify-espn-sports": { "url": "https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server" } } } ``` Then ask Claude to call `get_scoreboard`, `get_schedule`, `get_game_summary`, `get_standings`, or `get_teams` with the JSON input fields described above. ### Claude Code MCP usage For Claude Code, add the Apify MCP endpoint with: ```bash claude mcp add apify-espn-sports https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server ``` For agentic coding workflows, register the same MCP endpoint and ask the agent to call the sports tool before generating dashboards, alerts, or reports. Keep `maxItems` small for conversational calls and increase it for scheduled backfills. ### API usage #### JavaScript ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/espn-sports-mcp-server').call({ tool: 'get_scoreboard', league: 'football/nfl', maxItems: 25, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` #### Python ```python from apify_client import ApifyClient import os client = ApifyClient(os.environ['APIFY_TOKEN']) run = client.actor('automation-lab/espn-sports-mcp-server').call(run_input={ 'tool': 'get_standings', 'league': 'football/nfl', 'maxItems': 100, }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/automation-lab~espn-sports-mcp-server/runs?token=$APIFY_TOKEN" \ -H 'Content-Type: application/json' \ -d '{"tool":"get_schedule","league":"baseball/mlb","startDate":"20250601","endDate":"20250608","maxItems":150}' ``` ### Integrations - ๐Ÿ“ฐ Generate daily sports newsletters from scoreboard rows. - ๐Ÿ“ฃ Send Slack or Discord alerts for games that changed status. - ๐Ÿ“Š Load standings into Google Sheets, BigQuery, Snowflake, or Airtable. - ๐Ÿค– Give LLM agents a live sports context tool. - ๐Ÿงช Backfill historical schedule ranges for testing analytics pipelines. - ๐Ÿ” Chain `get_scoreboard` event IDs into `get_game_summary` calls. ### Tips for reliable runs - Use ESPN event IDs from `get_scoreboard` before calling `get_game_summary`. - Use `startDate` and `endDate` for MLB or schedule-heavy backfills. - Keep `includeRaw` off unless your workflow needs ESPN's original fragments. - If a league path returns no rows, verify ESPN supports that sport/league path publicly. - ESPN endpoints are undocumented, so field availability can vary by league and season. ### Troubleshooting #### Why did I get zero results? The selected date may not have games, the league may be out of season, or ESPN may not expose that league path through the same endpoint. Try `get_teams` or a known active league/date first. #### Why is `eventId` required for game summaries? ESPN game summaries are event-specific. Run `get_scoreboard` for the same league/date first, copy the `eventId`, then call `get_game_summary`. #### Why are odds missing? Odds are only returned when ESPN includes odds or pickcenter data for that event and region. The Actor emits odds rows/details when present; it does not synthesize odds. ### Legality ESPN Sports MCP Server reads public JSON endpoints and stores normalized records in your Apify dataset. Always review your use case, avoid excessive polling, honor ESPN's rights and terms, and comply with applicable law. The Actor is not affiliated with, endorsed by, or sponsored by ESPN. ### Legal and ethical use This Actor reads public ESPN JSON endpoints. Use the data responsibly, respect ESPN's terms, do not overload upstream services, and comply with applicable laws and platform rules. The Actor is not affiliated with ESPN. ### Related scrapers and tools Explore related Automation Lab actors at: - https://apify.com/automation-lab/google-trends-scraper - https://apify.com/automation-lab/website-contact-finder - https://apify.com/automation-lab/news-api-scraper ### FAQ #### Does this Actor require an ESPN API key? No. The MVP uses public ESPN JSON endpoints that responded without login during feasibility testing. #### Can I use this as an MCP server? Yes. Use Apify's MCP endpoint with `?tools=automation-lab/espn-sports-mcp-server` and pass one of the tool inputs. #### Can it cover every sport on ESPN? It supports any compatible ESPN `sport/league` path, but QA focuses on major leagues such as NFL, NBA, MLB, NHL, and major soccer. #### Does it scrape HTML pages? No. It uses HTTP JSON endpoints, which keeps runs faster and cheaper than browser scraping. #### Does it return raw data? Set `includeRaw` to `true` to include compact raw ESPN fragments on output rows. #### Can I schedule it? Yes. Use Apify schedules to run scoreboards daily or standings hourly during busy periods. #### What is the best default run? Use `get_scoreboard` with `football/nfl` and a small `maxItems` for a cheap smoke test, or use `baseball/mlb` with a date range for larger schedule exports. # Actor input Schema ## `tool` (type: `string`): MCP-style ESPN operation to run. ## `league` (type: `string`): ESPN sport/league path. Examples: football/nfl, basketball/nba, baseball/mlb, hockey/nhl, soccer/eng.1, soccer/usa.1. ## `date` (type: `string`): Optional ESPN scoreboard date. Leave blank for ESPN's default current/upcoming scoreboard. The prefill uses a known NFL game day so default health checks return records. ## `startDate` (type: `string`): Optional range start for scoreboards/schedules, for example 20250907. ## `endDate` (type: `string`): Optional range end for scoreboards/schedules, for example 20250914. ## `eventId` (type: `string`): Required for get\_game\_summary. Use get\_scoreboard first to discover event IDs. ## `maxItems` (type: `integer`): Maximum dataset records to emit. Keep this small for MCP agent calls; raise it for schedule backfills. ## `includeRaw` (type: `boolean`): Adds compact raw ESPN JSON fragments to each output item for advanced agent workflows and endpoint drift recovery. ## Actor input object example ```json { "tool": "get_scoreboard", "league": "football/nfl", "date": "20250907", "maxItems": 20, "includeRaw": false } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 ```javascript 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 = { "tool": "get_scoreboard", "league": "football/nfl", "date": "20250907", "maxItems": 20, "includeRaw": false }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/espn-sports-mcp-server").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python 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 = { "tool": "get_scoreboard", "league": "football/nfl", "date": "20250907", "maxItems": 20, "includeRaw": False, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/espn-sports-mcp-server").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "tool": "get_scoreboard", "league": "football/nfl", "date": "20250907", "maxItems": 20, "includeRaw": false }' | apify call automation-lab/espn-sports-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/espn-sports-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ESPN Sports MCP Server", "description": "MCP-ready ESPN sports tools for live scores, schedules, standings, teams, summaries, and odds from public ESPN JSON endpoints.", "version": "0.1", "x-build-id": "r7r2UglNbzCQGQcWM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~espn-sports-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-espn-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/automation-lab~espn-sports-mcp-server/runs": { "post": { "operationId": "runs-sync-automation-lab-espn-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/automation-lab~espn-sports-mcp-server/run-sync": { "post": { "operationId": "run-sync-automation-lab-espn-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", "required": [ "tool" ], "properties": { "tool": { "title": "๐Ÿงฐ Tool to call", "enum": [ "get_scoreboard", "get_schedule", "get_game_summary", "get_standings", "get_teams" ], "type": "string", "description": "MCP-style ESPN operation to run.", "default": "get_scoreboard" }, "league": { "title": "๐ŸŸ๏ธ ESPN sport/league", "type": "string", "description": "ESPN sport/league path. Examples: football/nfl, basketball/nba, baseball/mlb, hockey/nhl, soccer/eng.1, soccer/usa.1.", "default": "football/nfl" }, "date": { "title": "๐Ÿ“… Single date (YYYYMMDD)", "type": "string", "description": "Optional ESPN scoreboard date. Leave blank for ESPN's default current/upcoming scoreboard. The prefill uses a known NFL game day so default health checks return records." }, "startDate": { "title": "๐Ÿ“… Start date (YYYYMMDD)", "type": "string", "description": "Optional range start for scoreboards/schedules, for example 20250907." }, "endDate": { "title": "๐Ÿ“… End date (YYYYMMDD)", "type": "string", "description": "Optional range end for scoreboards/schedules, for example 20250914." }, "eventId": { "title": "๐ŸŽฏ ESPN event ID", "type": "string", "description": "Required for get_game_summary. Use get_scoreboard first to discover event IDs." }, "maxItems": { "title": "Maximum records", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum dataset records to emit. Keep this small for MCP agent calls; raise it for schedule backfills.", "default": 20 }, "includeRaw": { "title": "Include raw ESPN fragments", "type": "boolean", "description": "Adds compact raw ESPN JSON fragments to each output item for advanced agent workflows and endpoint drift recovery.", "default": false } } }, "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 } } } } } } } } } } ```