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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Get player prop history

propline_get_player_history
Read-onlyIdempotent

Player prop history across recent games. Returns each prior prop this player took with line, prices, resolution, and actual value. Pro tier returns full data; free tier returns redacted resolution/actual_value with an upgrade pointer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax number of past games (default 20, max 100)
marketsNoComma-separated subset of markets (e.g. 'player_points,player_rebounds')
sport_keyYes
player_nameYesPlayer name as it appears in box scores — e.g. 'Aaron Judge', 'Nikola Jokic'

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds valuable behavioral context about tier-differentiated data (full data vs redacted resolution/actual_value) and the upgrade pointer, going beyond the structured 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 is three concise sentences: it front-loads the core purpose, then details the return fields, and finally explains tier behavior. Every sentence adds value with no redundancy or filler.

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?

For a read-only tool with 4 parameters and no output schema, the description adequately explains the output fields, the scope ('recent games'), and the tier behavior. It could be improved with a concrete example or explicit default limit, but the schema already handles parameter constraints.

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?

The input schema covers 75% of parameters with descriptions (limit, markets, player_name), and the description does not add much parameter-specific meaning. 'Recent games' loosely hints at the limit parameter, but the description largely relies on the schema for parameter documentation.

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 'Player prop history across recent games' and 'Returns each prior prop this player took with line, prices, resolution, and actual value,' specifying both the resource and the exact output. This distinguishes it from siblings like propline_get_player_trends or propline_get_odds.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool over alternatives such as propline_get_player_trends or propline_get_event_results. It mentions tier-based limitations (Pro vs free) but lacks explicit 'use this when...' guidance or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All PropLine — Sports Betting Odds & Prop Resolution toolsInspect
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TDQS

A4.1/5.0
Disambiguation3/5

The tools are mostly distinct by purpose, but several overlap in areas like odds retrieval (get_odds vs get_best_line vs get_event_ev) and historical data (get_odds_history vs get_odds_closing vs export_odds_history). Descriptions are detailed and clarify distinctions, but the close functional relationships (e.g., get_event_movement vs get_odds_history) may cause selection ambiguity for an agent.

Naming Consistency4/5

The naming pattern is largely consistent: propline_<verb>_<noun> with verbs like get, list, export. Most tools follow this structure (e.g., get_event_results, list_events, list_sports). Deviations include 'propline_export_odds_history' (export instead of get) and a few longer names like 'propline_get_mlb_grand_salami' and 'propline_get_nhl_daily_goals_total' that break the simple verb_noun pattern but are still readable. Overall, the naming is predictable with minor exceptions.

Tool Count4/5

With 23 tools for a sports betting odds and prop resolution server, the count is on the higher side but still within a reasonable range given the domain's complexity (odds, EV, movement, results, player trends, webhooks, exports). Each tool serves a distinct function, though some could be consolidated (e.g., grand salami and NHL daily totals could be one). Slightly heavy but not excessive.

Completeness4/5

The tool set covers the core lifecycle: discover sports and events (list_sports, list_events), retrieve odds and markets (get_odds, list_event_markets), analyze EV and lines (get_event_ev, get_best_line, get_event_movement), track results and player stats (get_event_results, get_event_stats, get_player_history, get_player_trends), and backfill via exports. Missing features include webhook management (deliberately omitted) and possibly batch operations, but the surface is comprehensive for the stated purpose.

Resources