olympus-bets-analytics

get_track_record

Read-onlyIdempotent

Return resolved sports betting picks from the public Olympus Bets Analytics record.

Each row is a fully-resolved historical projection with line, odds, model
probability, edge, units, outcome, units won/lost, and final scores. The record is timestamped and publicly auditable. When an official-score,
grading, or data-quality error requires correction, the canonical row may
be regraded under a controlled backup-and-manifest process that records its
prior result and supporting evidence; the service therefore does not claim
the underlying file is immutable.

Args:
    league: Filter by league (NBA, NHL, MLB, CBB, NFL, SOCCER, LOL, GOLF, TENNIS).
    result: Filter to WIN, LOSS, or PUSH only.
    tier: Filter to public free rows, masked premium rows, or masked
        Premium Leans rows (``lean`` — flat 0.5u model disagreements with
        the price, graded in their own column, never blended into
        ``premium``; masked identically to premium rows since leans are
        paid content — matchup/result/units only, no line/odds/edge).
    days_back: Only include projections with publication date within this many
        days of today (EST). Default 30.
    limit: Maximum rows to return (capped at 500).
    cursor: Zero-based result offset for stable pagination.

Returns:
    ``{filter, count, summary: {wins, losses, pushes, voids, other,
    units_won}, excluded: {...}, picks: [...]}``
    ``total_matching`` always equals ``summary.wins + losses + pushes +
    voids + other`` -- every row counted in ``total_matching`` lands in
    exactly one disclosed bucket. ``excluded`` is a separate, all-time
    (not filtered by this call's args) count of what never reaches this
    population at all. Picks are newest-first.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tier`	No
`limit`	No
`cursor`	No
`league`	No
`result`	No
`days_back`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 17, 2026

changedInput schema / properties / tier / anyOf

Previous value: -[
-  {
-    "enum": [
-      "free",
-      "premium"
-    ],
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "enum": [
+      "free",
+      "premium",
+      "lean"
+    ],
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

Changed3 schema fields changedJul 24, 2026

addedInput schema / properties / cursor

Added value: +{
+  "default": 0,
+  "title": "Cursor",
+  "type": "integer"
+}

addedInput schema / properties / tier

Added value: +{
+  "anyOf": [
+    {
+      "enum": [
+        "free",
+        "premium"
+      ],
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Tier"
+}

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "get_track_recordDictOutput",
+  "type": "object"
+}

First observedJun 7, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses important behavioral traits: rows may be regraded under a backup-and-manifest process, the file is not immutable, tiers mask premium rows differently, picks are newest-first, and total_matching must equal the sum of disclosed buckets. This materially helps an agent trust and interpret results.

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 front-loaded with the purpose, then organized into Args and Returns sections that are easy to scan. Though lengthy, each paragraph earns its place, especially the tier explanation and regrading caveat, which are genuinely necessary for correct use.

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 definition covers all six parameters, output shape, ordering, filtering semantics, and the total_matching/excluded invariants. Even with the output schema present, it explains return structure enough that an agent could call and interpret the tool correctly without any additional documentation.

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?

Schema description coverage is 0%, so the description carries the full burden, and it delivers. It explains every parameter: league values, result restrictions, the nuanced tier options including lean handling, days_back's EST window and default, limit's cap, and cursor's zero-based pagination semantics. This adds substantial meaning beyond the bare 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 opens with a specific verb and resource: 'Return resolved sports betting picks from the public Olympus Bets Analytics record.' The qualifiers 'resolved' and 'public' clearly separate this from projection, recommendation, and personal-history siblings, so an agent can identify what this tool uniquely provides.

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 strong usage context by framing the tool as a filtered, historical, auditable record and explaining each filter. However, it does not explicitly name alternative tools or state when not to use it, leaving some routing against overlapping siblings like get_pick_history and get_projection_history to inference.

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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