NBA Prop Lab MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PROPLAB_API_KEY | No | Bearer API key. Required only for the research and backtest tools; public tools work with no key at all. | |
| PROPLAB_API_URL | No | API base URL. Default: https://nbaproplab.com. | https://nbaproplab.com |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| proplab_track_recordA | Get PropLab's historical pick performance: overall hit rate, profit, rating breakdown (Elite/Good/Marginal/Weak), daily results, and recent Good+ picks. Without a tier filter the overall/total fields cover ALL tiers (including internal Weak/Avoid picks that are never published) and the premium* fields cover Good+; with a tier filter EVERY headline field describes that tier only. Supports league filter (nba/wnba), tier filter, stat type, and date range. Public — no auth required. |
| proplab_dashboardA | Get today's (or a specific date's) dashboard: games with spreads/totals, top 10 scored picks, and rating distribution. Public — no auth required. |
| proplab_backtest_summaryA | Aggregate backtest over PREMIUM (Good+) picks only: total picks, hits, misses, pushes, hit rate, profit, and ROI for a date range. Requires auth (PROPLAB_API_KEY). Supports league filter. |
| proplab_backtest_dailyA | Day-by-day backtest results with hits, misses, daily profit, and cumulative profit. Requires auth (PROPLAB_API_KEY). Good for charting profit curves. |
| proplab_backtest_by_ratingA | Hit rate and profit broken down by pick rating tier (Elite, Good, Marginal, Weak, Avoid). Requires auth. Use to compare tier quality across leagues. |
| proplab_backtest_by_statA | Hit rate and profit broken down by stat market (Points, Rebounds, Assists, PtsRebAst, etc.). Requires auth. Use to find which markets the model performs best on. |
| proplab_pick_detailsA | Get full details for a specific pick by ID: player, stat, line, direction, score, 7-block breakdown, spider chart data, and settlement result. |
| proplab_evaluate_pickB | Score a custom pick on-demand through PropLab's 7-block engine. Returns a confidence score (0-100), rating, and block-by-block breakdown. Requires auth. |
| proplab_player_researchA | Deep research data for a player: season averages, recent game logs, matchup history, DvP, trends, and injury status. Use proplab_search_players first to get the playerId. |
| proplab_search_playersA | Search for NBA/WNBA players by name. Returns player ID, full name, team, and position. Use the returned ID with proplab_player_research or proplab_evaluate_pick. |
| proplab_gamesA | Get NBA/WNBA games for a date: teams, spreads, totals, game status. Defaults to today. |
| proplab_system_statusA | Check PropLab system health: API health status, recent pipeline runs, data freshness, and any errors. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 12 tools
While most tools have distinct purposes, there is notable overlap between proplab_track_record, proplab_backtest_summary, and proplab_backtest_by_rating, which all report historical hit rates and profit, and both track_record and backtest_by_rating provide rating-tier breakdowns. This could confuse agents selecting the right tool, though the descriptions help differentiate scope and auth requirements.
All tools use the consistent `proplab_` prefix and snake_case with clear verb_noun or noun_noun patterns (e.g., proplab_search_players, proplab_backtest_summary). No deviations in convention.
With 12 tools, the set is well-scoped for an NBA prop analytics server, covering games, picks, backtesting, and player research without excessive fragmentation or missing core areas.
The surface covers core workflows: game data, pick details, historical performance, backtesting, custom pick evaluation, and player research. Minor gap: no explicit tool to list all picks for a date (dashboard only shows top 10), but agents can work around via track_record or backtest tools.