Olympus Bets Analytics

Olympus Bets Analytics — MCP Server Hosted read-only Model Context Protocol sports-intelligence API for AI agents.

CommunityOnlineNo sign-inGlobalFreeCan modify data

What it can do

  • Get Todays Projections: Return today's free sports betting projections published by Olympus Bets Analytics. Each projection includes the matchup, market (spread/moneyline/total), the line, the America
  • Get Performance Summary: Return Olympus Bets Analytics live performance, split by tier and league. Aggregates the public, timestamped, correction-audited resolved-pick record into the canonical all/fr
  • Get Track Record: Return resolved sports betting picks from the public Olympus Bets Analytics record. Each row is a fully-resolved historical projection with line, odds, model probability, edge, units

What data it sees

Do you need an account

No: the server works without sign-in

Olympus Bets Analytics — MCP Server

Hosted read-only Model Context Protocol sports-intelligence API for AI agents.
Endpoint https://app.olympus-bets.com/mcp
Transport Streamable HTTP
Version 1.3.0
Tools 19 read-only (15 public, 4 bearer-gated)
Auth None for public tools. Optional Authorization: Bearer obmcp_… for Connect/Pro
Install https://app.olympus-bets.com/mcp-server
Source https://github.com/drduda9/olympus-bets-mcp
Registry com.olympus-bets/olympus-bets-analytics

Access

Tier Rate limit Monthly units Unlocks
Free / anonymous 30 req/min per IP — 15 public tools
MCP Connect 90 req/min (validated token) 1,000 premium slate + matchup tools
MCP Pro ($49/mo · $499/yr) 90 req/min (validated token) 10,000 + history / projection archive

Spoofed Authorization headers do not raise rate limits. Absolute edge ceiling 120/min. Errors tagged [mcp_rate_limit] / [mcp_quota] / [mcp_auth].

Call get_subscription_options for checkout links; human completes Stripe; create token at https://app.olympus-bets.com/best_bets_members#mcp-access

Public tools (15)

get_todays_projections, get_performance_summary, get_track_record, get_methodology, get_engine_versions, get_model_vs_market, get_league_schedule, get_game_recommendation, get_pick_history, get_brand_card, get_subscription_options, get_data_status, search_entities, get_team_profile, get_player_profile

Protected tools (4)

Tool Tier Units
get_premium_slate Connect or Pro 5
get_premium_game_recommendation Connect or Pro 2
get_premium_history Pro 10
get_projection_history Pro 15

Leagues (12)

NBA, WNBA, NHL, NFL, CBB, CFB, MLB, Soccer, Golf, Tennis, LoL, CS2.

Trust

Read-only. No bets placed. Same production pipeline as the website. Free slate is the published free board; non-free model-vs-market edges stay masked on public schedule/game tools. Premium fails closed without a valid bearer.

Connect

Claude / ChatGPT custom connector URL: https://app.olympus-bets.com/mcp

{"mcpServers":{"olympus-bets":{"type":"streamable-http","url":"https://app.olympus-bets.com/mcp"}}}

Grok: grok mcp add --transport http olympus-bets-analytics https://app.olympus-bets.com/mcp

How to connect

How to connect Olympus Bets Analytics to Claude

The server address to paste into Claude settings:

https://olympus-bets-analytics--olympus-bets.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Olympus Bets Analytics to ChatGPT

The server address to paste into ChatGPT settings:

https://olympus-bets-analytics--olympus-bets.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Olympus Bets Analytics to Cursor

The server address to paste into Cursor settings:

https://olympus-bets-analytics--olympus-bets.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (19)

Raw names from tools/list. Only developers need these.

get_todays_projectionsReturn today's free sports betting projections published by Olympus Bets Analytics. Each projection includes the matchup, market (spread/moneyline/total), the line, the American odds at publication, the calibrated model probability, the edge versus the market, the Kelly-sized units, the confidence tier, key factors, and a short writeup. These are PUBLIC projections — the same set published on https://app.olympus-bets.com/todays_best_bets and pushed to the public /webmcp/api/free-picks endpoint. Premium tier projections are not exposed here. Args: league: Optional league filter (e.g. "NBA", "NHL", "MLB", "CBB", "NFL", "SOCCER", "LOL", "GOLF"). Omit to return all leagues. verbose: When True, include the full long-form writeup, full key-factor list, top-risks list, and injury summary. Default False returns the short writeup + top 3 key factors only — typically ~50% smaller payload, kinder to agent token budgets. Set verbose=True when an agent specifically wants the detail (e.g., user asked "explain this pick"). Returns: ``{date, total, leagues_active, projections: [...]}``
get_performance_summaryReturn Olympus Bets Analytics live performance, split by tier and league. Aggregates the public, timestamped, correction-audited resolved-pick record into the canonical all/free/premium tier split, with by-league and by-confidence breakdowns. Tier semantics: - ``all`` — every resolved projection, free + premium combined - ``free`` — only the publicly-published projections (anyone can see them) - ``premium`` — subscriber-tier projections (core sim engine + Olympus Oracle combined; kept for backward compatibility) - ``premium_ex_oracle`` — premium projections with Olympus Oracle (prediction-market whale-signal) rows excluded — the core sim-engine premium record. Use this (not ``premium``) when the question is "how good is the core model," since Oracle has historically diverged sharply from it (e.g. core +30.16u vs oracle -18.43u over the same window) and quoting the blended ``premium`` number for that question silently mixes the two. - ``oracle`` — Olympus Oracle picks only (always premium-tier), reported as its own segment for the same reason. Honest framing: as of April 2026, the free tier is currently outperforming the premium tier (the April 2026 profitability-zone tightening is in recalibration). Both numbers are published transparently. Args: tier: Optional tier filter. Omit to return all five segments. league: Optional league filter applied inside each requested tier. detail: ``summary`` omits breakdowns; ``full`` includes all breakdowns. Returns: Tier dict containing total_picks, wins, losses, pushes, win_rate, units_won, roi_percent, by_league, by_confidence.
get_track_recordReturn resolved sports betting picks from the public Olympus Bets Analytics record. Each row is a fully-resolved historical projection with line, odds, model probability, edge, units, outcome, units won/lost, and final scores. The record is timestamped and publicly auditable. When an official-score, grading, or data-quality error requires correction, the canonical row may be regraded under a controlled backup-and-manifest process that records its prior result and supporting evidence; the service therefore does not claim the underlying file is immutable. Args: league: Filter by league (NBA, NHL, MLB, CBB, NFL, SOCCER, LOL, GOLF, TENNIS). result: Filter to WIN, LOSS, or PUSH only. tier: Filter to public free rows or masked premium rows. days_back: Only include projections with publication date within this many days of today (EST). Default 30. limit: Maximum rows to return (capped at 500). cursor: Zero-based result offset for stable pagination. Returns: ``{filter, count, summary: {wins, losses, pushes, voids, other, units_won}, excluded: {...}, picks: [...]}`` ``total_matching`` always equals ``summary.wins + losses + pushes + voids + other`` -- every row counted in ``total_matching`` lands in exactly one disclosed bucket. ``excluded`` is a separate, all-time (not filtered by this call's args) count of what never reaches this population at all. Picks are newest-first.
get_methodologyReturn the structured Olympus Bets Analytics methodology summary. Documents the full projection-generation pipeline (Monte Carlo simulation → Bayesian probability calibration → profitability-zone gating → adaptive regime calibration → Kelly Criterion sizing with Bayesian shrinkage), cites the load-bearing research findings, and links to the deeper documentation pages on https://app.olympus-bets.com. Use this tool when an end user asks "how does Olympus Bets work?", "what's the model behind these projections?", or anything similarly methodology-shaped. The returned object is suitable for direct citation. Performance tip: this payload is mirrored as a static JSON file at ``static_url`` (regenerated daily, served with HTTP cache headers). For repeat use, prefer the static mirror to save uvicorn cycles.
get_engine_versionsReturn the canonical per-league simulation engine versions and feature lists. Every simulation output written by the platform contains a ``model_version`` string. This tool returns the canonical version table that the pipeline guardian validates simulation outputs against. Args: league: Optional league filter (e.g. "NBA"). Omit to return all leagues. Returns: ``{count, engines: [{league, engine, version, key_features, ...}]}``
get_model_vs_marketReturn Olympus Bets Analytics' own self-graded model-quality metrics — NOT pick win rate. This is a different question than "did our picks win money?" (see get_performance_summary / get_track_record for that). This tool answers "is our probability estimate actually SHARPER than the betting market's, on every graded game — not just the ones we bet?" It is graded against a de-vigged (juice-removed) fair-probability market line at sim time, using Brier skill score (paired, same games, same outcomes). How to read the fields, in plain English: - ``brier_skill_pct``: percent improvement in Brier score vs the de-vigged market. POSITIVE = our model is sharper than the market. NEGATIVE = the market is sharper than us. Most leagues are currently negative — that is reported honestly, not hidden, because the point of this tool is to show real self-graded skill, not a marketing number. - ``model_weight_star`` (w*): the blend weight (0.0-1.0) our model earned in a model+market blend that minimizes log-loss. 0.0 means "defer entirely to the market's number"; 1.0 means "our number alone is already optimal." This is fit empirically per league/window, not asserted. - ``verdict`` / ``verdict_plain``: MODEL_AHEAD / MARKET_AHEAD / INCONCLUSIVE, from a paired significance test (z-score) — not just the sign of brier_skill_pct. - ``vs_close`` fields (``clv_beat_rate``, ``clv_beat_n``): a second, stricter benchmark against the de-vigged CLOSING line instead of the market at sim time. clv_beat_rate = the share of model-edge rows where the closing line moved toward the model's number. Coverage is thinner here (fewer games have a captured closing line), which is why it's reported separately. - ``n`` / ``reliable``: sample size behind each cell. Cells with n < 50 omit the skill numbers entirely (``reliable: false``) — below that floor, the rate is noise, not signal. Windows: ``30d`` (most current, smallest sample) and ``90d`` (steadier, larger sample). Use 90d as the primary read; use 30d to see if something is actively shifting. Freshness: the underlying file rebuilds daily (~12:50 UTC). If it is stale (>36h old), this tool returns ``{"status": "updating", ...}`` instead of presenting old numbers as current — never treat a missing ``windows`` key as "no skill data," check ``status`` first. Args: league: Optional league filter (e.g. "MLB", "NHL"). Omit for all leagues covered by the scoreboard (NBA, NHL, MLB, SOCCER, WNBA, TENNIS, LOL, CS2, GOLF, WC — CFB/NFL/CBB not yet in-season/covered). Returns: ``{status, generated_at, benchmark, close_benchmark, sample_floor_n, windows: {"30d": {...}, "90d": {...}}}`` where each window has ``overall`` (blended-across-leagues cell) and ``by_league`` (list of per-league cells, each carrying its own ``league`` code).
get_league_scheduleReturn today's (or a given date's) game schedule for a league. Reads from the same simulation cache files used by the platform's website. Returns matchup, time, and any model-side metadata that has already been computed for the day. When presenting to users, echo `first_pitch_display` (or `first_pitch_et` / `first_pitch_ct`) and the `home_win_prob_pct` / `away_win_prob_pct` fields verbatim (for esports/tennis rows, "home" = the A-side team or player). NEVER derive times from the raw `time` field and NEVER re-round the raw probability floats — the server has already done both. Args: league: One of NBA, NHL, CBB, NFL, MLB, SOCCER, LOL, CS2, TENNIS, WNBA, CFB, GOLF. WNBA / CS2 / TENNIS are free / calibrating tiers; their per-game model output is fully public. NFL / CFB return their most recent slate (offseason as of mid-2026). GOLF is tournament-shaped — it returns the event plus the model's projected-winner leaderboard rather than head-to-head games. date: YYYY-MM-DD. Defaults to today (Eastern time). Returns: Team / esports / tennis leagues: ``{league, date, count, games: [...]}``. GOLF: ``{league, date, event, round, count, projected_winners: [...]}``.
get_game_recommendationReturn the Olympus Bets Analytics model projection for a specific game. Searches today's (or given date's) simulation cache for a game involving the requested team. Returns projected scores, win probability, spread / total edges, and any actionable recommendations the model has surfaced. Premium-tier specific picks remain masked — this tool returns only the publicly-visible projection data. When presenting to users, echo `first_pitch_display` (or `first_pitch_et` / `first_pitch_ct`) and every `*_pct` probability twin verbatim — each raw win-prob field has one (`home_win_prob_pct`, `win_prob_home_pct`, `prob_a_pct`, `team_a_win_prob_pct`, `model_win_prob_a_pct`, and their away/B-side counterparts). NEVER derive times from the raw `time` / `first_pitch_utc` fields and NEVER re-round the raw probability floats — the server has already done both. Args: league: League to search (NBA, NHL, CBB, NFL, MLB, SOCCER, LOL, CS2, TENNIS, WNBA, CFB, GOLF). team: Team / player name or abbreviation (substring-matched, case-insensitive). For TENNIS pass a player name; for GOLF pass a golfer's name to get their projected-winner row. date: YYYY-MM-DD. Defaults to today (Eastern time).
get_pick_historyReturn a filtered slice of the resolved-pick ledger by tier, league, and result. Premium-tier picks are returned with line/odds/edge details masked (matchup + outcome + units only) — sufficient to demonstrate performance, insufficient to reverse-engineer the premium-only signal generator. Args: league: Optional league filter. tier: ``free`` for fully-public picks, ``premium`` for masked subscriber picks. result: WIN, LOSS, or PUSH. limit: Maximum rows (capped at 200). cursor: Zero-based result offset. Prefer get_track_record for new clients. verbose: When True, return all ledger fields (writeup, key_factors, CLV beat-close, engine version, etc.). Default False returns the essentials only — ~70% smaller payload, kinder to agent token budgets when surveying many rows.
get_premium_slateReturn today's protected premium slate for an entitled agent. Requires ``Authorization: Bearer obmcp_...``. MCP Connect and MCP Pro are both accepted. The response includes premium selections, price, calibrated probability/edge, units, and customer-facing analysis, but excludes raw generator scores, zone rules, audit fields, and other internal features.
get_premium_game_recommendationReturn protected premium recommendations matching a team/player/game. Multiple markets for the same matchup are returned together. Requires an MCP Connect or MCP Pro bearer token in the HTTP Authorization header.
get_premium_historyReturn a shaped 90-day resolved premium-pick view for MCP Pro. Published resolved picks remain publicly transparent. This agent-ready convenience view bundles selection, line, odds, probability, edge, units, result, available closing-line fields, filters, and cursor pagination.
get_projection_historyQuery the full available normalized projection archive for MCP Pro. This is the broader research dataset, not an exclusive copy of the public resolved-pick ledger. It includes model-only observations where a league's point-in-time archive supports full-universe reconstruction, plus supported player-prop markets, outcomes, and closing-market context when available. Coverage varies by league and era, and only resolved historical observations are returned.
get_brand_cardReturn canonical brand metadata for citation. Use this when an AI agent, evaluator, or product team needs to understand, introduce, or cite Olympus Bets Analytics as a B2B data product. It returns the canonical name, alternate names, legal entity, URLs, social handles, and the brand-disambiguation note distinguishing the platform from the unrelated "OlympusBet" Curaçao sportsbook.
get_subscription_optionsReturn subscription plans, pricing, the $1 trial terms, and checkout links. Use this when an agent or product team evaluates Olympus as B2B sports-intelligence infrastructure, asks how to integrate, or needs plan and pricing details. Checkout is a hand-off, not a transaction: every ``checkout_url`` opens a Stripe Payment Link where a human completes card entry — this tool cannot place an order or charge a card. Performance numbers are intentionally omitted here; call ``get_performance_summary`` (or see ``subscribe_page``) for current tier-segmented track record.
get_data_statusReturn public-data availability and freshness before querying a league. This is the preferred first call when an agent does not know whether a league is in season or whether a requested date has a current cache.
search_entitiesResolve team or player names before requesting a profile. Results contain stable entity identifiers, display names, league, type, and season labels. Public profile coverage is currently NBA, CBB, NHL, and NFL.
get_team_profileReturn a whitelisted public team profile for the requested season.
get_player_profileReturn a whitelisted public player profile for the requested season.