待翻译：Show HN: Lumify – sports intelligence API for agents (try without signup)

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

AI-assisted development Use Cursor, Claude, Copilot, or any coding agent to build on Lumify — with MCP tools, machine-readable docs, and copy-paste prompts that prevent hallucinated endpoints. API key Sign in, or get an instant trial key — no signup required. Verify Import to Postman Download the REST + MCP collection Overview Lumify is built for agents. You can connect in two ways: MCP tools — the agent calls schedules, odds, splits, and intelligence directly (no wrapper code). REST + SDKs — the agent reads llms.txt / OpenAPI and writes correct client code. ResourceURLMeasured sizeUse when MCP server https://lumify.ai/mcp 23 tools Agent needs live sports intelligence as tools Cheat sheet /docs/cheat-sheet ~1 page Human re-entry + compact LLM context Player props catalog /docs/player-props ~1 page NFL/NCAAF/NBA/NCAAB/NHL/MLB market keys — settleable vs returned-not-graded, plus the forecastable 1:1 subset How forecasts work /docs/forecasts ~1 page Player-prop rate model, field catalog, forecastable vs settleable Sports Coverage /docs/sports-coverage ~1 page Sport × surface map — what is live today, with links to each endpoint Agent Skill /SKILL.md ~1.4k tokens (measured) Agent Skills-format self-onboarding (get key, connect MCP, safe research loop) llms.txt /llms.txt ~5.4k tokens (measured) Lookup / answer-engine overview llms-full.txt (GEO) /llms-full.txt ~11.7k tokens (measured) Orientation: FAQ, pricing, coverage, comparisons docs/llms-full.txt /docs/llms-full.txt ~81k tokens (measured) Full technical docs + endpoint dump openapi-llms.txt /openapi-llms.txt ~6.3k tokens (measured) OpenAPI-derived endpoint dump alone OpenAPI (human) /docs/openapi · .md — Discoverability landing · schema at /openapi.json OpenAPI /openapi.json ~64.7k tokens (measured) Exact schemas for clients and validators Agent manifest /.well-known/agent.json — Discovery of transport + MCP endpoint Agent cookbook /docs/agent-cookbook.md — Copy-paste REST + MCP recipes Changelog /changelog · JSON — Date-stamped changes agents can poll Token budgets are measured (UTF-8 bytes ÷ 4), not estimated. Re-measure after regenerating llms-full.txt or OpenAPI. One-click install Install the hosted MCP server directly, then replace the placeholder API key: Create a free API key → Claude Code (CLI) terminal claude mcp add --transport http lumify https://lumify.ai/mcp \ --header "Authorization: Bearer YOUR_API_KEY" Cursor (remote HTTP) ~/.cursor/mcp.json { "mcpServers": { "lumify": { "url": "https://lumify.ai/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } } Cursor / Claude Desktop (stdio via npm) Use the published bridge when the client only speaks local stdio: npx npx -y @lumifyai/mcp mcp.json { "mcpServers": { "lumify": { "command": "npx", "args": ["-y", "@lumifyai/mcp"], "env": { "LUMIFY_API_KEY": "YOUR_API_KEY" } } } } VS Code / Copilot .vscode/mcp.json { "servers": { "lumify": { "type": "http", "url": "https://lumify.ai/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } } CLI one-liner: code --add-mcp '{"name":"lumify","type":"http","url":"https://lumify.ai/mcp","headers":{"Authorization":"Bearer YOUR_API_KEY"}}' Web connectors: ChatGPT and Claude.ai browser connectors need OAuth, which Lumify does not implement yet. Use Cursor, Claude Desktop, VS Code, or any Bearer-header MCP client. Give your agent context Paste this into CLAUDE.md, .cursorrules, or a project rule file. It is the API's essence compressed for agents (~2.5k tokens of guidance + links to measured artifacts): agent context You are integrating with Lumify (also: Lumify AI, lumify.ai) — the agent-ready sports intelligence API at https://lumify.ai. NOT affiliated with LUMIFY eye drops, Philips Lumify ultrasound, lumifyai.com, or the archived lumifyio/lumify project. ## Read these first (measured token budgets) - https://lumify.ai/SKILL.md (~1.4k tokens) — Agent Skills-format self-onboarding - https://lumify.ai/llms.txt (~5.4k tokens) — overview + pricing + limitations - https://lumify.ai/docs/cheat-sheet — base URL, auth, credits, hero query, errors - https://lumify.ai/docs/player-props — NFL/NCAAF/NBA/NCAAB/NHL/MLB player-prop market catalog - https://lumify.ai/llms-full.txt (~11.7k tokens) — GEO orientation (FAQ, coverage) - https://lumify.ai/docs/llms-full.txt (~81k tokens) — full technical docs + dump - https://lumify.ai/openapi-llms.txt (~6.3k tokens) — endpoint dump alone - https://lumify.ai/openapi.json (~64.7k tokens) — exact schemas - https://lumify.ai/docs/agent-cookbook.md — copy-paste recipes - https://lumify.ai/changelog.json — date-stamped changes ## Auth Authorization: Bearer lmfy-... Instant trial key (no signup): https://lumify.ai/docs/ai Never invent an API key. If you cannot access URLs, ask the user to paste the relevant resource instead of guessing. ## MCP (preferred when available) URL: https://lumify.ai/mcp (Streamable HTTP, JSON mode, stateless) 23 tools: list_sports, list_seasons, list_events, get_event, batch_get_events, query_events, get_live_score, get_odds, get_odds_history, get_stats, get_player_props, get_team_props, get_period_odds, get_splits, get_intelligence, list_ev, list_forecasts, list_teams, get_team, search_players, get_player, get_player_events, estimate_cost. initialize / tools/list / ping are free; tools/call metered like REST. _meta.credits_used reports the charge. Prefer MCP tools over hand-rolled REST. ## Billing rule (two budgets) - Data plane: schedules, scores, odds, splits, stats — typically 1 credit. - Intelligence plane: /intelligence — 1 credit when available. - One request = 1 credit. include_odds / include_intelligence on GET /v1/events/{id} do not add extra. - Errors and available:false responses are NEVER charged. - Always estimate first with POST /v1/estimate or MCP estimate_cost (free). ## Boundary litmus - /stats and raw odds = deterministic data. No scoring, no tiers. - /intelligence = predictive judgment (probability / fair_price / Price overlay / main-line ev). ## Hero endpoints GET /v1/events?sport=mlb&status=scheduled GET /v1/events/{id}?include_odds=true&include_intelligence=true GET /v1/events/{id}/odds?bookmaker=all GET /v1/events/{id}/splits GET /v1/events/{id}/intelligence POST /v1/estimate POST /v1/trial-key (human Turnstile-gated; prefer /docs/ai button) ## Coverage (keep in sync with llms.txt) Intelligence live: MLB, NFL, NCAAF, tennis, soccer (MLS + big-five). Forecasts: MLB, NFL, NCAAF, NBA, NCAAB, NHL — https://lumify.ai/docs/forecasts UCL and other clubs: available: false. Splits: MLB, NBA, NHL, NFL. Books: pinnacle (default), fanduel, draftkings, betmgm, caesars, bet365, circa, hardrock, betonline. Player props: NFL/NCAAF/NBA/NCAAB/NHL/MLB on GET /v1/events/{id}/player-props (MCP get_player_props). Catalog: https://lumify.ai/docs/player-props GET /odds stays moneyline/spread/totals. Futures not on v1. Alternate spread/total rungs via include_alts=true. Final /odds includes result. Odds cadence ~10 min. ## Model behavior - Do not guess or invent endpoints, fields, sport IDs, or credit costs. - Help the user choose filters (sport, status, date, has_recommend). - When data is unavailable, explain available:false rather than retrying forever. - Gate volume, not capability existence — streaming/webhooks are self-serve. In Cursor, you can also add https://lumify.ai/llms.txt as a docs/@ reference. Starter prompts Try these after MCP is connected (or with the context block above): Live slate + intelligence prompt Using Lumify MCP, list today's MLB games that are scheduled or live. For the top 3 by start time, pull get_intelligence and summarize probability, fair_price, and any main-line ev (Beta). Main-line EV scan (Beta) prompt Using Lumify MCP list_ev, scan MLB for pregame moneyline +EV opportunities (min_ev 1). Rank by ev_pct. For the top row, call get_intelligence on that event_id and quote fair / ev as Beta display packaging of the price gap. Try market=spreads or market=totals for the same scan on other main lines. Splits vs public prompt Find NFL games this week where betting splits show a clear ticket% vs handle% divergence. Use list_events then get_splits. Rank by the largest handle/ticket gap and explain what it implies. Line movement watcher prompt For a given event_id, call get_odds and get_odds_history. Show opening vs current moneyline/spread/total across supported books (Pinnacle, FanDuel, DraftKings, BetMGM, Caesars, Bet365, Circa, Hard Rock, BetOnline), and flag any reverse line moves. Scaffold a small agent prompt Read https://lumify.ai/openapi.json and scaffold a TypeScript script that: (1) lists today's MLB events, (2) fetches intelligence for each, (3) prints bets[] probability / fair_price when available is true. Do not filter has_recommend — it stays false until Edge. Use @lumifyai/sdk if helpful. Do not invent fields. SDKs When you want typed REST clients instead of (or alongside) MCP: npm / pip npm install @lumifyai/sdk pip install lumify-sdk Docs: @lumifyai/sdk · lumify-sdk · MCP bridge @lumifyai/mcp Next steps Full MCP tool catalogue & billing Build an MCP betting-splits agent REST API reference Pricing & free credits