DunkAPI

Prop probabilities

get_prop_probabilities

Read-onlyIdempotent

Use for what the market thinks of the player props in one game, not any one book's price: for each player and each line, the average implied probability (margin in) and the fair probability (margin out), with how many books each rests on. Only identical lines are combined, and a yes/no market whose books post only the yes price has no fair probability. Latest pre-game prices. For each book's prices use get_player_props. Costs 10 credits. Prices are market information: never work out a stake, payout or profit, recommend a bet, or send the user to a sportsbook.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "game_id": "<game_id>"
    },
    "name": "get_prop_probabilities"
  }
}

Input Schema

Name	Required	Description
`league`	No	League. Defaults to nba. One of: nba, wnba, ncaam.
`market`	No	One market, e.g. player_points. Default every market.
`game_id`	Yes	Game id, e.g. 401859967.
`player_id`	No	One player's id (e.g. 3945274, from search_players). Default every player.

Output Schema

Name	Required	Description	Default
`note`	No
`tool`	No
`count`	Yes
`props`	Yes
`method`	Yes
`credits`	No
`game_id`	Yes
`estimate_only`	No
`scheduled_start`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 7, 2026
- changedInput schema / properties / game_id / description
```
Previous value: -"Game id, e.g. 401704627."New value: +"Game id, e.g. 401859967."
```
AddedNov 7, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the descriptive bar is lower. The description still adds real behavioral context: aggregation only over identical lines, the yes/no market edge case where no fair probability exists, freshness ('latest pre-game prices'), and the credit cost. The usage-policy sentence about never computing stakes or recommending bets is behavioral guidance, though it reads more like policy boilerplate than tool mechanics.

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?

It is dense and front-loaded: the core purpose and the sibling routing appear in the first two clauses, followed by return semantics, edge cases, cost, and constraints. Every sentence carries information, but the sentence is long and over-stuffed with semicolons and parentheticals, and the compliance sentence is somewhat tangential to invocation.

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?

For a four-parameter, read-only aggregation tool with an output schema already defined, nothing needed to call it correctly is missing: scope, aggregation rules, edge cases, freshness, cost, and the alternative tool are all covered. The description does not need to restate return shapes because the output schema exists.

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 description coverage is 100% with two enums, so the schema already documents league, market, game_id and player_id, including defaults. The description reinforces the scoping ('one game', 'each player and each line') but adds no syntax, id-source, or format detail beyond what the schema states. Baseline 3 is correct here.

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 names a specific verb and resource ('what the market thinks of the player props in one game') and then precisely enumerates the returned quantities: average implied probability (margin in), fair probability (margin out), and book counts per line. It explicitly separates itself from the sibling that returns raw book prices ('not any one book's price... For each book's prices use get_player_props'), so an agent can route correctly without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states the positive case (aggregated consensus across books for one game), the negative case (single-book pricing), and names the exact alternative tool to use instead. It also adds a non-obvious operational constraint — a 10-credit cost — which lets the agent weigh whether the call is worth it.

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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