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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Get cross-book +EV

propline_get_event_ev
Read-onlyIdempotent

Pro-tier endpoint. Returns cross-book +EV per outcome for an event. We anchor on a sharp book, remove vig, derive a no-vig fair line, and compute EV% per book at the same line. Outcomes are sorted with +EV plays floated to the top of each line group. PrizePicks is excluded from EV math (DFS payouts aren't comparable to per-book prices). The anchor is chosen PER LINE in the order pinnacle → polymarket → kalshi → bovada → smarkets, and each line's fair_source names the one used — report the anchor from fair_source per line, never assume Pinnacle anchored all of them. Optional bookmakers filter prices to the books the user holds accounts at; it never changes the anchor, so filtering to DraftKings still measures DraftKings against Pinnacle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketsNo
event_idYes
sport_keyYes
bookmakersNoComma-separated book keys (e.g. 'draftkings,fanduel') to price only the user's books. Narrows prices, not the anchor.
min_ev_pctNoFilter to outcomes with EV ≥ this percent (e.g. 2.0).

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description goes well beyond annotations by disclosing concrete behavioral details: the anchor selection order per line, fair_source semantics, PrizePicks exclusion, outcome sorting, and the fact that bookmaker filtering does not change the anchor. This is rich, non-obvious behavioral information that an agent needs to interpret results correctly.

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: main purpose, methodology, sorting, exclusion, anchor-order caveat, and filter nuance. The most important caveat about fair_source is prominently included rather than buried.

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 complex EV endpoint with no output schema, the description provides substantial operational context: what fair_source means, how the anchor is chosen, and how filtering behaves. However, it does not describe the expected response structure or clarify the markets parameter, so it is not fully complete.

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

Parameters2/5

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

Schema description coverage is only 40%, so the description must compensate for the undocumented parameters. It adds valuable nuance to bookmakers, but it does not explain markets, sport_key, or event_id, and min_ev_pct is only covered by the schema. The description mentions 'event' generically but leaves real gaps for required and optional parameters.

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 ('Returns cross-book +EV per outcome for an event') and explains the methodology clearly. This purpose is distinct from sibling tools like get_best_line or get_odds because it is explicitly focused on EV calculation and per-book comparisons.

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 description gives clear context on when this tool is used: when cross-book EV per outcome is needed, with optional bookmaker filtering and min_ev_pct filtering. It does not explicitly name alternatives or state when NOT to use it, so it stops short of a 5, but the intended usage is apparent.

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