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PropLine — Sports Betting Odds & Prop Resolution

by io.github.proplineapi

Export odds history

propline_export_odds_history
Read-onlyIdempotent

Backfill-pass / Enterprise only. Bulk line-movement tick history as CSV — every recorded odds snapshot (price + line, per book, including period markets) across a whole sport, one row per (outcome, snapshot). This is the raw firehose no subscription tier can bulk-pull (Pro/Streaming use propline_get_odds_history per event instead). REQUIRES a since/until window to keep the pull bounded — the full archive runs to gigabytes per sport. The result is capped to the first 200 rows for context safety; for the full dataset use the /v1/exports/odds-history endpoint directly with curl/SDK and stream to disk. Non-entitled keys get a 403 with an upgrade pointer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceYesISO datetime lower bound on recorded_at (required, e.g. 2026-04-01T00:00:00Z). Keep the window narrow.
untilYesISO datetime upper bound on recorded_at (required, e.g. 2026-05-01T00:00:00Z).
marketNoOptional market key filter
bookmakerNoOptional bookmaker filter
sport_keyYesSport key, e.g. baseball_mlb

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds critical behavioral context: CSV output format, per-row granularity, row cap of 200, the need for a bounded window due to huge archive size, and the alternative endpoint for full dataset access. This transparently discloses limitations and error handling beyond the structured metadata.

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 yet efficient, with each sentence contributing essential information: access restrictions, format, scope, alternative, requirements, cap, endpoint workaround, and error behavior. It is front-loaded with the core purpose and avoids repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex export tool with no output schema and 5 parameters, the description covers all necessary context: what it returns (CSV rows), who can use it, why to use it, its limitations, and what happens on failure. It is fully self-contained for an agent to decide and invoke correctly.

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?

The schema already has 100% parameter description coverage, so the baseline is 3. The description adds extra value by explaining that since/until keep the pull bounded, advising 'Keep the window narrow', and clarifying that sport_key governs the whole-sport scope. This enriches parameter understanding beyond the schema.

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 specifies a clear verb and resource: 'Bulk line-movement tick history as CSV' with detailed scope ('every recorded odds snapshot ... across a whole sport'). It distinctly separates this from sibling tools like propline_get_odds_history, making its unique purpose unambiguous.

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

Usage Guidelines5/5

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

It explicitly states when to use this tool ('Backfill-pass / Enterprise only') and names the alternative for other tiers ('Pro/Streaming use propline_get_odds_history per event instead'). It also provides prerequisites (since/until window) and error behavior (403 for non-entitled keys), fully guiding 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