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Glama

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "get_methodologyDictOutput",
+  "type": "object"
+}
  2. First observed

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

A3.6/5.0
Disambiguation2/5

Several tools occupy overlapping historical/performance territory: get_track_record, get_pick_history, get_premium_history, get_projection_history, and get_performance_summary all query resolved picks or performance, and get_pick_history even instructs callers to prefer get_track_record. While the long descriptions help, the name-level boundaries are unclear and an agent selecting by tool name could easily choose the wrong history endpoint.

Naming Consistency5/5

Tool names follow a consistent get_<noun> snake_case convention, with search_entities as the only deviation but still a verb_noun form. The naming pattern makes the API predictable and scannable.

Tool Count3/5

Twenty tools is on the heavy side for a single MCP server and falls in the borderline 16-25 range. The count is not absurd, but several history/record tools could likely be consolidated into fewer endpoints.

Completeness4/5

The server covers the main analytics workflow well: data status, methodology, schedules, projections, premium content, performance, historical records, profiles, and subscription details. Minor gaps exist (e.g., no single all-league schedule endpoint, no generic search across all data types), but core querying needs are satisfied.

Resources