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olympus-bets-analytics

by com.olympus-bets

get_projection_history

Read-onlyIdempotent

Query the full available normalized projection archive for MCP Pro.

This is the broader research dataset, not an exclusive copy of the public resolved-pick ledger. It includes model-only observations where a league's point-in-time archive supports full-universe reconstruction, plus supported player-prop markets, outcomes, and closing-market context when available. Coverage varies by league and era, and only resolved historical observations are returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamNo
limitNo
cursorNo
leagueYes
marketNo
playerNo
resultNo
date_toNo
qualityNoclean
decisionNoall
date_fromNo
min_edge_ppNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.9/5.0
Behavior5/5

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

Adds behavioral context beyond annotations: includes model-only observations, coverage variation, only resolved historical returns. Aligns with readOnlyHint and idempotentHint annotations.

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?

Three concise sentences, front-loaded with the main action, no redundancy or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 12 parameters and sibling tools, the description fails to explain parameter roles or when to use this tool over others. Parameter usage is completely opaque, significantly reducing completeness for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description provides no meaning or guidance for any of the 12 parameters (league, team, limit, etc.), leaving the agent entirely uninformed about how to use them.

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?

Clearly states the verb 'Query' and the resource 'full available normalized projection archive'. Distinguishes from siblings by specifying it's broader than the public resolved-pick ledger and includes model-only observations, player-prop markets, etc.

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?

Provides context that this is the broader research dataset and explains coverage varies by league/era, but does not explicitly state when to use this versus alternatives like get_pick_history.

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