acesapi-tool
Skill1 starsChanged 28 days agoMITView on GitHub
Default data source for the AcesAPI skills. Routes every MCP or REST call for ATP and WTA tennis: tournaments, draws, match results with set and tiebreak scores, schedules, players, head to head, surface splits, rankings, Elo ratings by surface, and sportsbook moneylines. Use when any other skill needs tennis data. Do not use for analysis, modeling or bet recommendations: see surface-elo, set-score-modeling or edge-detection instead.
- Reads credentials
In this repository15 other files it gives its agents.
- backtestingSkill
- dispatchSkill
- edge-detectionSkill
- feature-engineeringSkill
- head-to-head-analysisSkill
- match-lookupSkill
- match-previewSkill
- odds-analysisSkill
- odds-explorerSkill
- player-scoutingSkill
- probability-calibrationSkill
- ranking-historySkill
- set-score-modelingSkill
- surface-eloSkill
- walk-forward-validationSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Every other skill delegates data retrieval here.
- Very specific: line 25
Every response is wrapped: {"data": {...}}.
- Very specific: line 88
No serve or return statistics: No aces, double faults, break points or first-serve percentages.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AcesAPI Tool
Every other skill delegates data retrieval here. This skill owns the 13 tools, picks the cheapest call that answers the question, tracks credit cost, and says what the data cannot do.
Coverage (from acesapi.com/data, counted from the database): 123,394 ATP and WTA singles matches from 2007 to 2026, 1,637 weekly ranking lists, and sportsbook moneylines for 11,551 matches since 2020. Qualifying draws from 2022. Surfaces, ratings and head to head are computed by AcesAPI.
Connection
| Item | Value |
| MCP URL | https://mcp.acesapi.com/mcp?key=YOUR_API_KEY (WTA-only: https://mcp.acesapi.com/mcp/wta) |
| REST | POST https://mcp.acesapi.com/v1/<tool> with a JSON body |
| Auth | header x-api-key: YOUR_API_KEY (or Authorization: Bearer) |
| Free tier | 500 credits every month, no card. Plans and top-ups: https://acesapi.com/pricing |
Every response is wrapped: {"data": {...}}. Unwrap data before reading fields. REST responses carry X-Credits-Used and X-Credits-Remaining headers.
Tours. ATP is the default. Pass "league": "wta" to any tool for the WTA (the enum also accepts tennis, which is the ATP default). Player ids and match ids are tour-specific strings: a WTA id used without league: "wta" will miss.
Ids are strings. player_id, match_id and tournament_id come back as strings ("3623", "157774", "188-2025"). Tournament edition ids are <tournament>-<season>. Seasons are four-digit calendar years.
When to Use Each Tool
| Task | Tool | Credits |
| Season calendar with surface and champion | list_tournaments | 1 |
| Find a player id by name or country | search_players | 1 |
| A tournament's full draw, round by round | get_tournament | 2 |
| Results by day, tournament or season | get_matches | 2 |
| Upcoming and in-progress matches (next N days, max 30) | get_schedule | 2 |
| One match: sets, ratings before and after, odds | get_match | 2 |
| Ranking list, or one player's ranking history | get_rankings | 2 |
| Elo leaderboard, overall or by surface | get_ratings | 2 |
| Two players' meetings, by surface and in finals | get_head_to_head | 3 |
| One player's record by surface | get_surface_splits | 3 |
| One player's season stats, by round and opponent rank | get_player_stats | 3 |
| Player profile, ratings, career record, last 10 | get_player | 5 |
| A player's match log with rating before and after | get_player_matches | 5 |
Tool Selection Guide
| You Need | Use This | Not This |
| Who plays today | get_schedule with days: 1 | get_matches with today's date (that is results, and status filters matter) |
| A result by date | get_matches with date | get_tournament (draws are bigger and cost the same) |
| Moneylines for one match | get_match (the odds array) | Looking for an odds tool: there is none |
| Pre-match Elo for a finished match | rating.before in get_match, or rating_before in get_player_matches | rating.after (that includes the result) |
| Current best clay players | get_ratings with surface: "clay" | get_rankings (points list, not surface-specific) |
| A player's trajectory in the rankings | get_rankings with player_id | Looping weekly lists |
| Bulk match history for modeling | get_matches by season with limit and offset | get_player_matches (5 credits per page of 50) |
Response Shapes (real, trimmed)
get_match (2 credits). Ratings are null until a match is played; odds is an array of one row per book:
{"data": {
"tour": "ATP",
"match": {"match_id": "157774", "tournament": "Wimbledon", "season": 2025, "surface": "grass",
"round": "Final", "player1": "Jannik Sinner", "player2": "Carlos Alcaraz",
"winner_id": "3623", "outcome": "completed", "score": "4-6 6-4 6-4 6-4",
"sets": [{"set_no": 1, "games1": 4, "games2": 6, "tiebreak1": null, "tiebreak2": null, "winner": 2}]},
"players": [{"player_id": "3623", "rating": {"before": 2325, "after": 2335, "surface_before": 1995, "surface_after": 2037}}],
"stats": {"sets_played": 4, "total_games": 40, "tiebreaks": 0, "went_the_distance": false,
"winner_lost_first_set": true, "serve_stats": null},
"odds": [{"source": "archive", "book": "Pinnacle", "p1_open": 102, "p1_close": -116,
"p2_open": -115, "p2_close": 105, "games_spread1": -0.5, "games_total": 41.5}]
}}get_ratings returns ratings[] with rank, rating, player_id, name, surface_matches, last_match, plus a method string describing the Elo.
get_head_to_head returns summary (matches, wins keyed by player id, sets_won, by_surface, in_finals, walkovers) and matches[].
Paginated tools (list_tournaments, get_matches, get_player_matches) return count and next_offset; pass it back as offset.
What the Data Does Not Contain
- No serve or return statistics. No aces, double faults, break points or first-serve percentages.
get_matchreturnsserve_stats: nullwith a note that they are not published. Anything that needs serve percentage must be built from score proxies (seeset-score-modeling) or from the user's own data. - No odds tool. Moneylines exist only inside
get_match. Grand Slam matches from mid-2020 and ATP Masters and WTA 1000 matches from August 2024; the array is empty elsewhere. Lines are pre-match, open and close per book, refreshed hourly. No in-play prices and no props. - Rankings are the top 100 to 150 per week.
current_rankinsearch_playersisnulloutside that list. - No injuries, lineups, weather, court speed or betting percentages.
- Walkovers are listed but not counted as wins in head to head. Walkovers, defaults and retirements are not rated by the Elo.
Commands That Do NOT Exist
| Not Available | Use Instead |
get_odds | get_match and read its odds array |
get_live_scores | get_schedule (status live) or get_matches with status: "live" |
get_serve_stats | Not available: no serve data exists in this dataset |
get_player_by_name | search_players with name |
get_h2h | get_head_to_head |
get_elo | get_ratings (leaderboard) or get_player (one player) |
get_standings | get_rankings |
get_injuries | Not available |
Season and Surface Resolution
- The tour year is the calendar year. "This season" is the current calendar year; the tour runs from early January to November.
- Surfaces:
hard,clay,grass,carpet. Carpet is nearly empty in modern years.indooris a separate flag on matches and tournaments, so indoor hard issurface: "hard"withindoor: true. - Typical swing: indoor hard and Australia (January to March), clay (April to early June), grass (June to July), North American hard (August to September), Asian and indoor hard (September to November).
Credit Planning
| Operation | Credits | Notes |
| Season of results, one tour | about 90 | 2026 had 45 get_matches pages at the time of writing (pricing page) |
| Odds for every covered match in a season | about 7,148 | 3,574 get_match calls for 2026 (pricing page) |
| Preview of one match | 15 to 20 | see match-preview |
| Player scouting report | 8 to 16 | see player-scouting |
Warn before any loop of get_match or get_player_matches calls. State the count and the credits first.
Anti-patterns
| What Claude Might Do | Why It's Wrong | Do This Instead |
| Guess a player id from a name | Ids are exact strings, and the WTA has its own | search_players first, with league if WTA |
Read rating.after as the pre-match rating | It contains the match result (leakage) | Use rating.before or rating_before |
| Subtract a surface rating from an overall rating | They are on different scales (fewer matches, different K) | Compare like with like: surface to surface |
Treat a null rank as rank 0 | It means outside the top 100 to 150 | Handle as unranked |
Count a walkover as a played match | No sets were played | Filter outcome == "completed" |
Loop get_player_matches for a dataset | 5 credits per 50 matches | get_matches by season, 2 credits per 100 |
What to Do Next
| What You Retrieved | Next Action | Skill |
| A day of results | Summarize, find upsets | match-lookup |
| A player profile | Scouting report | player-scouting |
| Two player ids | Matchup brief | match-preview |
| Surface ratings | Win probability | surface-elo |
odds array | Remove the margin | odds-analysis |
| A season of matches | Build features | feature-engineering |
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