olympus-bets-analytics

get_game_recommendation

Read-onlyIdempotent

Return 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). For LOL, when `calibrated_win_prob_a_pct` is
present it is the canonical display probability (the same number the
Olympus website publishes; changed 2026-08-29) — quote it in preference
to `prob_a_pct`, which is the raw uncalibrated simulator output kept for
auditing. In every league, `display_win_prob_home_or_a_pct` /
`display_win_prob_away_or_b_pct` is the canonical number to quote: the same
one get_league_schedule and the website show (for CS2 the market-anchored
`anchored_win_prob_a_pct`, not the raw `team_a_win_prob_pct`). A row carrying `quality_flags` (e.g. "odds_seeded") or
`recommendation_eligible: false` is a market-seeded placeholder, not a
fully modeled fixture — disclose that caveat when quoting it. 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).

Input Schema

Name	Required	Description	Default
`date`	No
`team`	Yes
`league`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJul 24, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "get_game_recommendationDictOutput",
+  "type": "object"
+}

First observedJun 7, 2026

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the readOnly/idempotent annotations: discloses premium-tier masking, that rows with quality_flags or recommendation_eligible:false are market-seeded placeholders needing a caveat, and explicit prohibitions (never derive times, never re-round floats). That is exactly the non-obvious behavior an agent needs before quoting results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening purpose and premium-masking sentences are front-loaded and tight, but the middle block balloons into a long enumeration of probability field twins and repeated canonical-display rules. Much of it is output-formatting instruction that a reader must wade through to reach the concise Args section.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Since an output schema exists, the description needn't define returns, yet it supplies the behavioral caveats (masking, placeholder rows) an agent can't get from the schema. Minor gap: no guidance on ambiguity when a team substring matches multiple games, and no mention of alternate siblings for comparison views.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden and does: it enumerates the accepted league values, explains that team is a case-insensitive substring match with league-specific meaning, and specifies date as YYYY-MM-DD defaulting to today in Eastern time.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Return the Olympus Bets Analytics model projection for a specific game") plus what is returned (projected scores, win probability, spread/total edges, recommendations). It also separates itself from the sibling get_premium_game_recommendation by declaring that premium-tier picks stay masked.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied — search today's or a given date's simulation cache for a game involving the team — and league-specific cases (TENNIS player name, GOLF golfer) are spelled out. But it never says when to prefer this over get_todays_projections, get_premium_game_recommendation, or get_league_schedule as an alternative source; get_league_schedule is only cited as a consistency reference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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