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olympus-bets-analytics

by com.olympus-bets

get_engine_versions

Read-onlyIdempotent

Return 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, ...}]}``

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leagueNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds behavioral context by explaining the tool's role in validating simulation outputs against the version table, which goes beyond the 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 concise, front-loads the purpose, and includes a brief background. However, the use of backticks and formatting for the return type adds minor clutter; could be slightly more streamlined.

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?

For a simple tool with one optional parameter and an output schema indicated, the description covers all essential aspects: purpose, input filter, and output structure. It is complete and leaves no ambiguity.

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

Parameters4/5

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

The schema has 0% description coverage, leaving the description to explain the single parameter. It does so effectively by describing the league filter with an example and noting the effect of omission, adding meaning beyond the schema's property title.

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 title and description clearly state that the tool returns the canonical per-league simulation engine versions and feature lists. It provides specific context about the model_version string and pipeline guardian validation, making the purpose distinct from sibling tools which focus on other data.

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 explains when the tool is useful (to retrieve the canonical version table for validation) but lacks explicit guidance on when not to use it or alternatives. It mentions an optional league filter but no further usage context.

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