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olympus-bets-analytics

by com.olympus-bets

get_oracle_board

Read-onlyIdempotent

Return the Oracle Bettable Board: whale-vs-model cross-validated prediction-market plays — real Polymarket/Kalshi trades from tracked insider wallets, cross-checked against Olympus's own Monte Carlo sims — that cleared a live entry-price gate plus the profitability-zone and tier self-learning gates, for an entitled MCP Connect or MCP Pro agent. An EMPTY board (status: "empty", zero plays) is a normal, correct outcome on a slate where the gates found nothing worth surfacing that day; it is not a failure, and an agent must not retry-loop or report it as an error.

Every play is sized at a flat 0.5 unit via ``components.oracle_board.
board_play_units()`` — deliberately never a Kelly/tier-derived stake.
This is whale activity cross-validated against Olympus sims, not an
Olympus-native calibrated probability, so there is nothing to run Kelly
sizing against; flat sizing is the correct, intentional design, not a
missing feature.

Plays are ordered by event start time only — this is explicitly NOT a
quality ranking. ``compound_confidence`` and any board-rank score are
excluded from both the ordering and this response on purpose (measured
at AUC 0.48-0.51 in production, no better than a coin flip); do not
infer that a play earlier in the list is a better bet than one later in
it.

Requires ``Authorization: Bearer obmcp_...``. MCP Connect and MCP Pro
are both accepted.

Args:
    sport: Optional sport filter (e.g. "NBA", "ESPORTS"). Omit for all sports.
    limit: Max plays to return (1-60; the board itself never exceeds 60 plays).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sportNo

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?

Even with readOnlyHint and idempotentHint annotations, the description adds substantial behavioral context: empty board semantics, flat 0.5 unit sizing rationale, event-time ordering with explicit disavowal of quality ranking, and the AUC evidence supporting why confidence scores are omitted. This far exceeds what annotations provide.

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 front-loaded with the core purpose and structured into logical paragraphs for behavioral notes and args. It is longer than strictly necessary, but every sentence adds essential information for correct invocation and interpretation. The explicit 'Args' section is clean and readable, though some prose could be tightened.

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, the description covers all essential aspects: what the board is, authorization requirements, parameter semantics, empty-state behavior, sizing, and ordering. An output schema exists, so return values need not be described, and the description fills every other gap comprehensively.

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?

With 0% schema description coverage, the description fully compensates by documenting both parameters: sport is explained as an optional filter with examples ('NBA', 'ESPORTS') and an omit-for-all instruction; limit is described with a valid range (1-60) and a note about the board's own cap. This provides meaningful semantics 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 opens with 'Return the Oracle Bettable Board' and provides a precise, detailed definition of what that board contains (whale-vs-model cross-validated prediction-market plays). This is a specific verb+resource that distinguishes it from sibling tools like get_premium_slate or get_model_vs_market by describing the unique gating and data sources.

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 gives clear context on when to use the tool (entitled MCP Connect/Pro agent) and provides important usage caveats: empty board is a normal outcome, ordering is not quality ranking, and flat sizing is deliberate. However, it does not explicitly mention alternative tools or when-not-to-use scenarios relative to siblings, so it falls short of the 'explicit when-not/alternatives' bar.

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