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olympus-bets-analytics

by com.olympus-bets

get_performance_summary

Read-onlyIdempotent

Return 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: all-time and rolling regimes are both available. Core
Premium and Oracle are separated so legacy or source-specific performance
cannot obscure the current production system. Both are published.

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.
    window: ``all`` preserves the historical contract; rolling windows use
        the same canonical ledger, grading, tier, and source rules.

Returns:
    Tier dict containing total_picks, wins, losses, pushes, win_rate,
    units_won, roi_percent, by_league, by_confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
detailNosummary
leagueNo
windowNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses substantial behavioral context: data source is 'correction-audited', tier semantics reflect historical divergence (e.g., core +30.16u vs oracle -18.43u), and both regimes are published. This helps the agent understand the tool's internal distinctions and honest framing, going well beyond annotations.

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

Conciseness5/5

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

The description is front-loaded with a clear purpose, then uses structured sections (aggregation, tier semantics, honest framing, Args, Returns) that each earn their place. Despite its length, every sentence adds necessary context for a complex multi-tier reporting tool, with no fluff.

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

Completeness5/5

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

Given four optional parameters and an output schema, the description covers all parameter semantics, explains return shape, and provides the broader context of why tier separation exists. It is complete for an agent to select and correctly invoke the tool.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: tier's enum meanings, detail's summary/full modes, window's historical vs rolling behavior, and league as a filter. The Args section adds semantic meaning that the schema lacks entirely.

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?

The first sentence clearly states the function: 'Return Olympus Bets Analytics live performance, split by tier and league.' This specifies the verb (return), resource (performance), and scope (split by tier and league), distinguishing it from siblings like get_track_record or get_pick_history. The detailed tier semantics further reinforce its unique purpose.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use specific tier values, e.g., 'Use this (not premium) when the question is "how good is the core model"' and explains why oracle is separated. However, it does not explicitly mention alternative tools or when not to use this tool overall, so it lacks explicit exclusion/alternative naming.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists (e.g., get_todays_projections vs get_game_recommendation and get_track_record vs get_pick_history vs get_performance_summary). However, the detailed descriptions help differentiate them.

Naming Consistency5/5

All tools follow a consistent 'get_*' or 'search_*' verb_noun pattern with snake_case. The only deviation is 'search_entities', which is a natural fit for a search operation.

Tool Count5/5

19 tools is well-scoped for a sports betting analytics server, covering metadata, data status, schedules, projections, performance, subscriptions, and profiles without being excessive.

Completeness4/5

The tool surface covers core analytics workflows (projections, track record, performance, methodology) plus supporting operations (brand, status, subscriptions, search). Minor gaps like league standings or team statistics are outside the primary scope.

Resources