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JacobiusMakes

parlayapi-mcp

parlayapi_consensus

Compare average, best, and worst odds across bookmakers for any market, giving a sharp baseline for line-shopping. Includes separate home, draw, and away rows for soccer.

Instructions

Get consensus (average) odds across all bookmakers per market.

Returns average, best, and worst price per (event, market, player, line), a sharp baseline for line-shopping. Soccer and other 3-way markets return separate home, draw, and away consensus rows.

Args: sport_key: e.g. "baseball_mlb", "soccer_epl". markets: Comma-separated market_keys (optional), e.g. "moneyline,totals".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketsNo
sport_keyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the burden of explaining behavior. It clearly discloses the return structure: average, best, and worst price per event/market/player/line, plus the special split for soccer and other 3-way markets. It does not mention auth or rate limits, but the core behavioral contract is transparent.

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 and front-loaded: the main action appears in the first sentence, return behavior in the second, the 3-way market edge case in the third, and parameters are compact and useful. There is no fluff or repetition of schema mechanics.

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?

The description is complete for a simple read-style endpoint: it explains what the tool returns, defines inputs, and gives a recommended use case. Since an output schema exists, extra return-value detail is not required. It could slightly strengthen by explicitly routing the agent away from sibling tools, but it is functionally complete.

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 fully compensates by explaining both parameters. It gives concrete examples for sport_key and clearly documents markets as an optional comma-separated list with example values like 'moneyline,totals'.

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 states a specific verb and resource: 'Get consensus (average) odds across all bookmakers per market.' It also clarifies the unique value of the tool by naming the output concepts—average, best, and worst price per (event, market, player, line)—which distinguishes it from siblings like get_odds or best_line.

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 usage context: it is 'a sharp baseline for line-shopping' and explains optional market filtering. It does not explicitly name alternatives or state when not to use it, but the purpose and filtering behavior are clear enough to guide an agent.

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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