Skip to main content
Glama

ParlayAPI

by io.github.JacobiusMakes

parlayapi_get_odds

Get game-level odds for a sport from all configured bookmakers.

Args:
    sport_key: e.g. "baseball_mlb", "soccer_epl"
    markets: Comma-separated. h2h, spreads, totals.
    regions: us, us2, uk, eu, fr, au, ca, mx, latam, br, asia. Comma-separated for multiple.
    bookmakers: Comma-separated bookmaker keys (optional).
        Examples: pinnacle, draftkings, fanduel, bovada, caesars.
    odds_format: decimal | american

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketsNoh2h
regionsNous
sport_keyYes
bookmakersNo
odds_formatNodecimal

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

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the scope ('from all configured bookmakers') and the output type ('game-level odds'), which is useful, but it does not disclose behaviors like data freshness, pagination, or whether the call is a live versus cached read.

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 compact and well-structured: a one-sentence purpose followed by a tight Args list. Every line contributes necessary information without unnecessary prose.

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 that an output schema exists, the description does not need to explain return values. It covers all input parameters and the core behavior. It is slightly incomplete only in lacking alternative-tool routing and any caveats about bookmaker configuration or data availability.

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%, and the description fully compensates by documenting every parameter: sport_key examples, comma-separated markets, accepted regions, optional bookmakers with examples, and odds_format choices. This adds substantial meaning beyond the bare schema titles and defaults.

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: 'Get game-level odds for a sport from all configured bookmakers.' This clearly identifies the tool's function and scope, and the phrase 'game-level odds' helps distinguish it from sibling tools like parlayapi_get_props.

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

Usage Guidelines2/5

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

The description gives no explicit guidance on when to use this tool versus alternatives such as parlayapi_best_line, parlayapi_consensus, or parlayapi_get_props. Usage context is only implied by the tool name and resource scope, not stated.

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

All ParlayAPI 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

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