Skip to main content
Glama

PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Get futures

propline_get_futures
Read-onlyIdempotent

Free-tier endpoint. Returns season-long futures (outright) markets for a sport — championship/Super Bowl/division/conference winners, MVP and award winners, season win totals — aggregated across Bovada, FanDuel, DraftKings, Pinnacle, and Kalshi. One row per (futures event, book, market) with each team/player outcome and its price. Marquee markets (Super Bowl winner, MVP, division/conference) are quoted by multiple books for comparison; exotic markets are often single-book. Useful for: 'who are the Super Bowl favorites across books', 'NFL MVP odds', 'NBA championship futures'. Futures are unresolved (no settlement grade).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sport_keyYesSport key — e.g. football_nfl, basketball_nba, baseball_mlb.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Although annotations already mark this as read-only and idempotent, the description adds meaningful behavior: it is free-tier, aggregates across five books, emits one row per futures event/book/market, and notes that marquee markets have multi-book coverage while exotic ones are often single-book. It also states that futures are unresolved, which is valuable operational context.

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 dense but every sentence earns its place: tier, resource, market examples, row structure, book comparison behavior, and resolved/unresolved state. It is front-loaded with the core purpose and avoids filler.

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?

With no output schema, the description compensates by explaining the row shape, outcome/price content, book aggregation, and settlement state. For a one-parameter read-only tool, this is sufficient for an agent to select and call it correctly.

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 single sport_key parameter already has 100% schema description coverage with examples. The description reinforces that the parameter selects a sport but adds no new parameter-level semantics beyond the schema, so the baseline of 3 is appropriate.

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 opens with a specific verb and resource: it returns season-long futures/outright markets for a sport. It enumerates exact market types and books, making the tool's purpose unmistakable and clearly distinct from sibling odds/results tools.

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 'Useful for:' section gives concrete query intents such as Super Bowl favorites and NFL MVP odds, making the intended use clear. It does not explicitly name alternatives or state when not to use this tool, but the market-type context strongly implies where it fits.

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