Skip to main content
Glama

PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

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'
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. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. The description adds significant behavioral detail beyond that: the return fields (over/under/push splits, streak, average actual stat, recent line), the trend windows (5/10/20/50), and how dfs_odds_type modifies computation. It doesn't mention empty-result behavior, which is a minor gap.

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 sentences, front-loaded with the core purpose, and includes concrete examples and usage notes. Every sentence contributes distinct value without redundancy or fluff.

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?

No output schema exists, so the description appropriately enumerates the returned data and explains the trend computation logic. It covers market scoping and dfs_odds_type nuance. It doesn't discuss sport_key format or empty-result behavior, but for a read-only data query with strong annotations, it's sufficiently complete.

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?

Schema coverage is 75% (3 of 4 params have descriptions). The description adds meaning to 'market' by explaining scoping and to 'dfs_odds_type' by describing the line-comparison behavior, which goes beyond the schema. However, the required 'sport_key' parameter lacks both schema description and description text, leaving a small but relevant gap.

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 uses a specific verb+resource: 'Hit-rate trends / last-N-games over rate for a player,' clearly defining what the tool does. It distinguishes itself from sibling tools by highlighting it's 'unique to PropLine's prop-resolution data' and provides a concrete consumer-facing phrasing ('did X go over in N of his last M 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?

It gives clear context for when to use the tool: 'Omit market for all markets, or pass one to scope' and explains the conceptual surface. However, it doesn't explicitly name alternative tools or state negative usage conditions, so it lacks the explicit when-not-to-use guidance needed for a 5.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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