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olympus-bets-analytics

by com.olympus-bets

get_methodology

Read-onlyIdempotent

Return the structured Olympus Bets Analytics methodology summary.

Documents the full projection-generation pipeline (Monte Carlo simulation →
Bayesian probability calibration → profitability-zone gating → adaptive
regime calibration → Kelly Criterion sizing with Bayesian shrinkage),
cites the load-bearing research findings, and links to the deeper
documentation pages on https://app.olympus-bets.com.

Use this tool when an end user asks "how does Olympus Bets work?",
"what's the model behind these projections?", or anything similarly
methodology-shaped. The returned object is suitable for direct citation.

Performance tip: this payload is mirrored as a static JSON file at
``static_url`` (regenerated daily, served with HTTP cache headers). For
repeat use, prefer the static mirror to save uvicorn cycles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint; the description adds that the payload is mirrored statically and suitable for citation, providing extra 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 detailed but front-loaded with the core purpose. It could be slightly more concise, but every sentence adds value (pipeline, usage, caching tip).

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 no parameters and an output schema, the description covers all necessary context: what is returned, when to use, and a caching suggestion. It is fully adequate for agent invocation.

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?

With zero parameters, the description has no burden to explain them. Baseline for no params is 4; the description appropriately focuses on the tool's output.

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 explicitly states it returns a structured methodology summary and enumerates the pipeline steps (Monte Carlo simulation, Bayesian calibration, etc.), clearly distinguishing it from sibling tools like get_performance_summary or get_todays_projections.

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?

It specifies when to use (user asking about methodology) and provides a performance tip to use the static mirror. While it doesn't explicitly list when not to use, the context is sufficient.

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