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Glama

ParlayAPI

by io.github.JacobiusMakes

parlayapi_find_middles

Find cross-book middle opportunities.

A middle takes the Over at a low line on one book and the Under at a
higher line on another, so a window of whole numbers cashes BOTH bets
(e.g. Over 7.5 at one book, Under 9.5 at another means 8 or 9 wins both).
Scans game totals, spreads, AND player-total props (points, rebounds,
strikeouts, ...). Each result carries the window, the numbers that hit,
and per-$100 economics: profit_if_hit and net_if_above/below_window.

Args:
    sport_key: e.g. "baseball_mlb", "basketball_nba".
    min_gap: Minimum window width in points/runs/goals. Default 1.0.
    markets: Comma-separated market_keys to limit the scan (optional),
        e.g. "totals" or "player_points".
    include_props: Include player-total props alongside game lines. Default True.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketsNo
min_gapNo
sport_keyYes
include_propsNo

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

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains what the tool scans, what a middle is, and what each result contains, including the economic fields. It does not mention rate limits, auth needs, or whether it reads live odds, but for a search/find tool the disclosed behavior is substantive and non-misleading.

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 longer than strictly necessary, but every major section earns its place: the concept explanation, the scan scope, the output summary, and the parameter semantics. The main purpose is front-loaded and the Args section is cleanly structured, though the extended example sentence could be trimmed without losing much value.

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 four-parameter find tool with an output schema, the description covers the key context: when to use it, what it scans, what results contain, and what each parameter means. It does not enumerate all possible market_keys or failure modes, but those are not essential given the output schema and examples.

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%, so the description must document the parameters. It does so thoroughly: sport_key has examples, min_gap has units and default, markets explains comma-separated format and gives example values, and include_props explains what it controls and its default. This fully compensates for the absent schema descriptions.

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: 'Find cross-book middle opportunities.' It then defines the concept precisely with a concrete example and names the exact scan scope (game totals, spreads, player-total props), making it easy to distinguish from other sports-betting tools.

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 clearly implies when to use the tool: when the user wants middle opportunities across books. However, it does not explicitly compare with sibling tools like parlayapi_find_arbitrage or parlayapi_find_ev, nor does it state when not to use this tool. The usage context is clear, but alternatives are not addressed.

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