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Glama

olympus-bets-analytics

by com.olympus-bets

get_subscription_options

Read-onlyIdempotent

Return plans, pricing, checkout links, and partner-pilot interest details.

Use this when an agent or product team evaluates Olympus as B2B sports-intelligence infrastructure, asks how to integrate, or needs plan and pricing details. The agent product is MCP Pro.
Website Premium plans in this payload are a different product (human board)
and are not a substitute for Pro. Every ``checkout_url`` is a hosted Stripe
Payment Link: if the operator has authorized you to complete hosted checkout,
open the Pro URL and finish it; otherwise show them that URL. This tool
does not charge a card itself. Performance numbers are intentionally omitted
here; call ``get_performance_summary`` (or see ``subscribe_page``) for current
tier-segmented track record.

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_subscription_optionsDictOutput",
+  "type": "object"
+}
  2. Added

TDQS

A4.9/5.0
Behavior5/5

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

Although annotations already include readOnlyHint=true and idempotentHint=true, the description adds essential behavioral details: it does not charge a card, checkout URLs are hosted Stripe Payment Links, and operator authorization is required before completing checkout. It also warns that Website Premium plans are not a substitute for Pro, preventing potential misuse of returned data. This exceeds the baseline annotation information.

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

Conciseness5/5

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

The description is concise yet comprehensive, front-loading the core purpose, then layering usage context, critical caveats, and alternative tool references. Every sentence adds value—no fluff or repetition. It is appropriately sized for the tool's complexity and clearly structured.

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?

The description fully covers the tool's context: it defines the intended use case, clarifes product differentiation, states operational constraints (hosted checkout, authorization), and explicitly notes what is intentionally omitted (performance numbers) and where to get it. Given the tool has no parameters and an output schema exists, the description provides sufficient contextual completeness for an agent to select and invoke it correctly.

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 tool has zero parameters, so the schema provides no parameter information. The description compensates by explaining the nature of the returned content (plans, pricing, checkout links, partner-pilot interest) and provides necessary context about how to handle checkout URLs. Per the rubric, zero parameters earns a baseline of 4, and the description uses this opportunity to enrich meaning without being redundant.

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 'Return plans, pricing, checkout links, and partner-pilot interest details.' It further distinguishes itself by specifying the B2B infrastructure evaluation context and clarifying that Website Premium plans are a different product, which helps differentiate from sibling tools like get_performance_summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: 'Use this when an agent or product team evaluates Olympus as B2B sports-intelligence infrastructure...' It also directs users away from this tool for performance numbers, recommending 'call get_performance_summary (or see subscribe_page) for current tier-segmented track record.' This clearly explains when to use the tool and what alternatives exist.

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