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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Get odds history

propline_get_odds_history
Read-onlyIdempotent

Hobby+ endpoint. Returns the historical line-movement snapshot series for an event (every recorded price/point change per outcome over the event's lifetime). Free tier returns market structure with redacted snapshots and an upgrade pointer. Supports period-historical filters: from/to (absolute ISO), relative_from/relative_to (offsets to commence_time like '-3h' or '0'), interval downsample ('30s'/'1m'/'5m'/'15m'/'30m'/'1h'), and changes_only=true to drop unchanged adjacent snapshots.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoISO timestamp; only include snapshots at or before. Mutually exclusive with relative_to.
fromNoISO timestamp; only include snapshots at or after. Mutually exclusive with relative_from.
periodNoGame-period filter. Omitted = full-game markets only. Canonical codes (q1..q4, h1/h2, p1..p3, i1..i9, f3/f5/f7), comma-separated, or 'all'.
marketsNo
event_idYes
intervalNoDownsample bucket. Latest snapshot per bucket wins.
sport_keyYes
bookmakersNoComma-separated subset of book keys (e.g. 'draftkings,fanduel'). Default returns all available.
relative_toNoOffset to commence_time, e.g. '-1m' or '0'. Mutually exclusive with to.
changes_onlyNoWhen true, drop snapshots whose (price, point) match the previous one.
relative_fromNoOffset to commence_time, e.g. '-3h', '-30m', '-90s'. Mutually exclusive with from.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds value by disclosing free-tier redaction behavior and filter semantics (e.g., changes_only drops unchanged snapshots, interval downsampling). No contradiction 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?

Three sentences pack a clear purpose, tier limitations, and filter list. Each clause adds meaningful detail without fluff. Slightly dense but appropriately sized for a complex 11-parameter endpoint.

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 tool with 11 parameters and no output schema, the description explains what is returned (historical snapshot series, market structure in free tier) and key filters. It lacks explicit return shape details but compensates with practical usage guidance.

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

Parameters4/5

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

Schema coverage is 73%, so the description carries some weight. It explains relative_from/to offsets ('-3h' or '0'), interval downsampling ('latest snapshot per bucket wins'), and changes_only behavior. These details enrich the schema's terse 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 clearly states a specific action: 'Returns the historical line-movement snapshot series for an event' with details about price/point changes. It distinguishes from siblings by emphasizing the snapshot series and filters, differentiating from export/current odds tools.

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?

Provides clear usage context: 'Hobby+ endpoint', free tier limitations with 'redacted snapshots' and an upgrade pointer, and lists applicable filters. It does not explicitly name alternatives but implies when to use via filter semantics and tier constraints. No explicit exclusions, but context is sufficient.

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