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olympus-bets-analytics

by com.olympus-bets

get_track_record

Read-onlyIdempotent

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, 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
limitNo
cursorNo
leagueNo
resultNo
days_backNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: it explains the regrading process for corrections, the controlled backup-and-manifest procedure, and that the service does not claim immutability. This is valuable for an agent to understand the data's integrity and auditability, going beyond the readOnlyHint and idempotentHint annotations.

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

Conciseness4/5

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

The description is well-structured with a main paragraph and Args/Returns sections. It is detailed but some sentences (e.g., the regrading process) are lengthy yet necessary for transparency. Overall, it is efficient and front-loaded with the core purpose.

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 the tool has 6 parameters, an output schema exists, and no nested objects, the description covers all essential aspects: parameter meanings, return format (filter, count, summary, excluded, picks), pagination details, and special behaviors like regrading and excluded buckets. It is thorough for an agent to use correctly.

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 schema description coverage at 0%, the description fully compensates by explaining each parameter's purpose, filter options, defaults (e.g., days_back default 30, limit capped at 500, cursor for pagination), and behavior. This provides meaning beyond the bare schema types and names.

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 description clearly states the tool returns resolved sports betting picks from the public Olympus Bets Analytics record, including detailed fields and filtering parameters. It distinguishes from sibling tools like get_pick_history by specifying 'fully-resolved historical projections' and mentioning 'public' and 'auditable', making the purpose distinct.

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?

The description implies usage for retrieving filtered track record data but does not explicitly compare to alternatives like get_pick_history or get_performance_summary. No guidance on when to use this tool over others is provided, only the parameter explanations.

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