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Glama

ParlayAPI

by io.github.JacobiusMakes

parlayapi_get_props

Get player prop odds for a sport.

Args:
    sport_key: e.g. "baseball_mlb", "basketball_nba"
    markets: Comma-separated prop market keys (optional).
        Examples: player_points, player_rebounds, player_strikeouts,
        player_passing_yards, player_total_bases.
    bookmakers: Comma-separated. Examples: prizepicks, underdog,
        sleeper, draftkings, fanduel.
    player: Filter to props mentioning this player name.
    include_futures: Include season-long futures (default False).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playerNo
marketsNo
sport_keyYes
bookmakersNo
include_futuresNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool gets prop odds and documents parameters; it does not mention response structure, data freshness, whether odds are live, or any side effects of include_futures. This is minimal disclosure beyond the obvious read operation.

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?

The description is efficient: one purpose sentence followed by a cleanly formatted Args block with examples. Every line earns its place and there is minimal fluff. The formatting is scannable and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values don't need to be described, but the description lacks guidance on which sibling tools to use for non-prop odds or how bookmaker coverage is determined. For a five-parameter tool with no annotations, the parameter documentation is strong, but the missing sibling context and operational details make it only moderately complete.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by explaining every parameter with concrete examples: sport_key values, comma-separated market keys, bookmaker names, player name filtering, and the default behavior of include_futures. This adds substantial meaning beyond the bare input schema and leaves no parameter ambiguous.

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 opens with a specific verb+resource statement: 'Get player prop odds for a sport.' This clearly identifies the tool's function and narrows its scope to player props, which helps differentiate it from general odds tools. However, it does not explicitly name or contrast with sibling tools like parlayapi_get_odds, so the differentiation is implicit rather than stated.

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 parameter examples (markets, bookmakers, player) imply use cases such as filtering by market or player, but the description never states when to choose this tool over alternatives like get_odds, best_line, or consensus. There is no explicit when-to-use/when-not-to-use guidance, leaving the decision to inference.

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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TDQS

A3.7/5.0
Disambiguation3/5

Most tools map to distinct workflows (raw odds, best-line, EV scan, arb, middle, single-bet grade, parlay grade), but several pairs are easy to mix up: find_ev vs best_bets both surface +EV opportunities, live_sports vs list_sports differ only in 'live', and verdict vs parlay_verdict have near-identical names. The detailed descriptions resolve most ambiguity, so it is not chaotic, but the boundaries are not all crisp.

Naming Consistency3/5

All names share the parlayapi_ prefix and snake_case, but the suffix style is inconsistent: some are verb-led (get_odds, find_arbitrage, set_bettable_books) and many are bare noun phrases (consensus, verdict, source_quality, magic_link). The live_* and best_* groups are internally consistent, but pairs like list_sports/live_sports and verdict/parlay_verdict add confusion.

Tool Count3/5

22 tools is on the heavy side for an MCP server, even though the sports-betting domain is broad. Each tool has a plausible purpose, but the public demo/metadata tools (live_command_center, book_coverage, source_quality, live_sports) could probably be consolidated or separated.

Completeness4/5

The surface covers the core domain well: sport discovery, game odds, props, consensus, best-line, EV, arbitrage, middles, single-bet verdicts, parlay verdicts, and account/signup flows. Minor gaps exist (no explicit book/market metadata list, no historical odds, no betting-account history), but agents can usually work around them.

Resources