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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Get NHL daily goals total

propline_get_nhl_daily_goals_total
Read-onlyIdempotent

Free-tier endpoint. Returns the synthetic daily NHL goals total (hockey's equivalent of the MLB Grand Salami) for a given UTC date — total goals scored across every NHL game on the slate (including OT/SO) plus each book's implied Daily Goals Total line (median of per-game primary totals across our NHL books). No retail sportsbook quotes this as a single market. Useful for: 'what's the total goal line for tonight's full NHL slate', 'did the Daily Goals Total go over yesterday', 'historical NHL daily-goals results for backtesting'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoYYYY-MM-DD UTC date. Defaults to today (UTC) when omitted.

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already disclose readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context: 'Free-tier endpoint', 'synthetic' nature, inclusion of OT/SO goals, and the calculation method (median of per-game totals). No contradictions with annotations.

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 four sentences, each contributing value: access tier, definition, calculation, and use cases. It is slightly more verbose than strictly necessary but well-structured and free of filler.

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 moderate-complexity tool with no output schema, the description explains both the returned metric (total goals plus each book's implied line) and its derivation, giving an agent a complete understanding of what to expect. It is sufficient for the tool's niche.

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 schema covers 100% of parameters with a clear description ('YYYY-MM-DD UTC date, defaults to today UTC'). The description reinforces the UTC date but adds no new parameter-specific detail beyond the schema, so it does not exceed the baseline.

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 ('Returns') with a precisely defined resource ('synthetic daily NHL goals total') and states its scope ('total goals scored across every NHL game on the slate'). It distinguishes from siblings by referencing the MLB Grand Salami and noting that no retail sportsbook quotes this as a single market, making it uniquely identifiable.

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 provides clear 'Useful for:' examples (slate total, over/under, backtesting) that indicate when to use the tool. Although it does not explicitly mention alternatives or when-not-to-use, the context of a niche synthetic market is sufficient to guide an agent's selection.

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
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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