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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi
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Get player trends

propline_get_player_trends
Read-onlyIdempotent

Hit-rate trends / last-N-games over rate for a player — unique to PropLine's prop-resolution data. For each market the player has graded history in, returns over/under/push splits across the last 5/10/20/50 graded games, current streak, average actual stat, and the recent line. This is the 'did X go over in N of his last M games?' surface. Omit market for all markets, or pass one to scope (e.g. 'player_points', 'batter_hits').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoOptional single market to scope trends to (e.g. 'player_points'). Omit for all markets.
sport_keyYes
player_nameYesPlayer name as it appears in box scores — e.g. 'Aaron Judge', 'Nikola Jokic' — or a player_id from propline_search_players, e.g. 'mlb:592450'
dfs_odds_typeNoOptional PrizePicks pick-em flavor. When set, the trend is computed against that flavor's PrizePicks line only (e.g. compare a player's goblin-line hit-rate vs his standard-line trend). Omit for the default cross-book behavior.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / player_name / description
      Previous value: -"Player name as it appears in box scores — e.g. 'Aaron Judge', 'Nikola Jokic'"New value: +"Player name as it appears in box scores — e.g. 'Aaron Judge', 'Nikola Jokic' — or a player_id from propline_search_players, e.g. 'mlb:592450'"
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description adds rich behavioral detail beyond the readOnly/idempotent annotations: it returns over/under/push splits across 5/10/20/50 games, includes streak and average actual stat, defaults to all markets, and explains the dfs_odds_type cross-book behavior. This gives an agent a strong model of what the call will do.

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, front-loaded with the core concept, and every sentence adds value. It avoids repeating schema boilerplate while still giving a complete picture of output scope and parameter behavior.

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 the tool's moderate complexity and the absence of an output schema, the description adequately lists what the response contains and how market and dfs_odds_type affect results. It does not describe exact response formatting or error cases, but an agent has enough to call it correctly.

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

Parameters4/5

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

The input schema already covers most parameters well, and the description reinforces market scoping with concrete examples ('player_points', 'batter_hits'). It also clarifies the all-markets default behavior, which adds meaning beyond the bare 'optional' schema wording. The sport_key parameter has no schema description, but its name and the player_name examples make it unambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: computing hit-rate trends and last-N-games over rates for a player, with specific output fields. It is easy to distinguish from generic stats tools, though it does not explicitly name sibling alternatives like propline_get_player_history or propline_get_player_games.

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: 'This is the did X go over in N of his last M games? surface.' It also explains how to scope by market and the default all-markets behavior. However, it does not explicitly state when not to use this tool or point to a specific alternative.

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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