Skip to main content
Glama

olympus-bets-analytics

by com.olympus-bets

get_league_schedule

Read-onlyIdempotent

Return 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: [...]}``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
leagueYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description details that the tool reads from simulation cache files, returns model-side metadata, and explains league-specific behaviors (e.g., GOLF returns projected winners). Since annotations already indicate readOnly and idempotent, the description adds valuable behavioral context without contradiction.

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 clear opening statement, behavioral notes, usage instructions, and parameter/return details. Every sentence adds value, but it could be slightly more concise in the behavioral section without losing clarity.

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's complexity (multiple leagues with varying behaviors, output structures), the description covers all necessary aspects: purpose, parameters, return format, special cases, and usage instructions. With annotations and output schema present, it is fully complete.

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?

Despite 0% schema description coverage, the description thoroughly explains both parameters: the 'league' enum with all values and special notes (e.g., WNBA/CS2/TENNIS free, GOLF different output), and the 'date' parameter with format and default. This adds significant meaning beyond the raw schema.

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 a game schedule for a league on a given date, using specific verbs ('return') and resources ('game schedule'). It distinguishes itself from sibling tools, which focus on other functionalities like brand cards or recommendations.

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 context for when to use the tool (get schedule for a league/date) and gives important usage instructions for presenting output (e.g., never derive times from raw 'time' field). However, it does not explicitly mention when not to use or suggest alternatives, though siblings do not directly compete.

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

All olympus-bets-analytics toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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