kenpom
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OV_PASSWORD | No | overtime.ag password for odds | |
| KENPOM_API_KEY | Yes | Your KenPom API key | |
| KENPOM_OUT_DIR | No | Output directory (default: data) | |
| OV_CUSTOMER_ID | No | overtime.ag customer ID for odds | |
| KENPOM_MAX_RETRIES | No | Max retry attempts (default: 5) | |
| KENPOM_RATE_LIMIT_RPS | No | Requests per second (default: 2.0) | |
| KENPOM_CACHE_TTL_SECONDS | No | Cache TTL in seconds (default: 21600) |
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": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| kenpom_teamsB | Get team rosters with coach, arena, and conference info for a season. |
| kenpom_conferencesB | Get list of conferences for a season. |
| kenpom_ratingsB | Get current efficiency ratings (AdjOE, AdjDE, AdjEM) for all teams. This is the core KenPom ranking data. |
| kenpom_predictionsB | Get KenPom game predictions (FanMatch) for a specific date. Includes predicted scores, win probability, and spread. |
| kenpom_fourfactorsB | Get Four Factors data: eFG%, TO%, OR%, FT Rate for offense and defense. These are the key stats that determine game outcomes. |
| kenpom_pointdistB | Get point distribution data: percentage of points from FTs, 2-pointers, and 3-pointers for offense and defense. |
| kenpom_heightB | Get height, experience, and roster continuity data for teams. |
| kenpom_miscstatsB | Get miscellaneous stats: shooting percentages, block/steal rates, assist rates, 3-point attempt rates. |
| kenpom_archiveA | Get historical ratings from a specific past date. Use this for backtesting or historical analysis. |
| kenpom_matchupA | Compare two teams head-to-head with key metrics side by side. Great for analyzing upcoming games. |
| kenpom_top_teamsB | Get top N teams by a specific metric (AdjEM, AdjOE, AdjDE, etc.) |
| kenpom_projectC | Project game score, margin, total, and win probability for a matchup. Uses OE/DE crossover method with configurable home court advantage. |
| kenpom_slateB | Build full projection slate table for a date. Returns all games with projected scores, margins, win probabilities, and optional market odds. Use backtest=true for time-correct archive features. |
| classify_effortA | Classify a query's effort level for dynamic model routing. Returns recommended effort level (low/medium/high), model hint, and thinking budget. Use this to determine appropriate reasoning depth before executing complex tasks. |
| get_tool_effortB | Get the effort level metadata for a specific tool. Returns the tool's default effort level and whether it requires reasoning. |
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 15 tools
Most tools have distinct purposes like ratings, predictions, or team rosters. However, 'kenpom_predictions' and 'kenpom_project' and 'kenpom_slate' overlap slightly in providing game forecasts, and the two effort-classification tools are unrelated to basketball data, causing potential confusion.
Thirteen tools follow the 'kenpom_' prefix with noun-based names, but 'classify_effort' and 'get_tool_effort' break the pattern. Some compound names lack underscores (e.g., 'fourfactors', 'miscstats') while others use them, creating minor inconsistency.
With 15 tools covering ratings, four factors, predictions, rosters, and more, the count is well-scoped for a college basketball analytics server. Each tool serves a clear function without redundancy.
The tool set covers core KenPom data like efficiency ratings, four factors, and game predictions. Missing are raw game results or player-level stats, but for team-level analysis the surface is nearly complete.