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kingplaybookHQadmin1

kingsplaybook-mcp

Get canonical game lines

kingsplaybook_get_lines
Read-onlyIdempotent

Retrieve aggregated moneyline, spread, and total lines for NBA, MLB, or NHL games on a chosen date, compiled across tracked sportsbooks.

Instructions

KingsPlaybook's canonical game-market lines — moneyline, spread, and total — aggregated across tracked sportsbooks, per game on a league + date. Requires a Pro plan or higher.

Args: league ('nba' | 'mlb' | 'nhl'), date ('YYYY-MM-DD'). Returns JSON: { data: [ { event_id, home_team, away_team, markets: [ { market_type, selections: [ { selection, line, prices: [ { book, price, decimal } ] } ] } ] } ], meta }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesGame date, ISO-8601 (YYYY-MM-DD)
leagueYesLeague: nba, mlb, or nhl
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe read-only nature is known. The description adds valuable context: the Pro plan requirement, aggregation by sportsbook, and a detailed return JSON structure, which goes beyond the 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?

The description opens with a clear, front-loaded summary sentence, then uses structured Args and Returns sections. Every sentence serves a purpose and there is no redundant or filler content.

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

Completeness4/5

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

Given a simple 2-parameter schema, rich annotations, and an inline return structure that acts as a de facto output schema, the description is nearly complete. Minor gaps like handling of empty data or the contents of 'meta' do not significantly detract.

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

Parameters3/5

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

Schema coverage is 100%; both league and date are fully described in the input schema. The description's Args line merely restates the enum and date format, adding no significant new meaning. Baseline 3 is appropriate.

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 a specific verb+resource: retrieves canonical game-market lines (moneyline, spread, total) per game by league and date. It distinguishes this from sibling tools like get_lineups and get_projections by naming the exact data types.

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 clear context: access requires a Pro plan, and it returns lines aggregated across sportsbooks per league and date. It does not explicitly name alternative tools, but the scope strongly implies when it should be used.

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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