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noahowshnoahowsh/acesapi-mcp/skills/acesapi-tool/SKILL.md

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

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
  1. Distinctive:

    Every other skill delegates data retrieval here.

    line 12
  2. Very specific:

    Every response is wrapped: {"data": {...}}.

    line 25
  3. Very specific:

    No serve or return statistics: No aces, double faults, break points or first-serve percentages.

    line 88

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

ItemValue
MCP URLhttps://mcp.acesapi.com/mcp?key=YOUR_API_KEY (WTA-only: https://mcp.acesapi.com/mcp/wta)
RESTPOST https://mcp.acesapi.com/v1/<tool> with a JSON body
Authheader x-api-key: YOUR_API_KEY (or Authorization: Bearer)
Free tier500 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

TaskToolCredits
Season calendar with surface and championlist_tournaments1
Find a player id by name or countrysearch_players1
A tournament's full draw, round by roundget_tournament2
Results by day, tournament or seasonget_matches2
Upcoming and in-progress matches (next N days, max 30)get_schedule2
One match: sets, ratings before and after, oddsget_match2
Ranking list, or one player's ranking historyget_rankings2
Elo leaderboard, overall or by surfaceget_ratings2
Two players' meetings, by surface and in finalsget_head_to_head3
One player's record by surfaceget_surface_splits3
One player's season stats, by round and opponent rankget_player_stats3
Player profile, ratings, career record, last 10get_player5
A player's match log with rating before and afterget_player_matches5

Tool Selection Guide

You NeedUse ThisNot This
Who plays todayget_schedule with days: 1get_matches with today's date (that is results, and status filters matter)
A result by dateget_matches with dateget_tournament (draws are bigger and cost the same)
Moneylines for one matchget_match (the odds array)Looking for an odds tool: there is none
Pre-match Elo for a finished matchrating.before in get_match, or rating_before in get_player_matchesrating.after (that includes the result)
Current best clay playersget_ratings with surface: "clay"get_rankings (points list, not surface-specific)
A player's trajectory in the rankingsget_rankings with player_idLooping weekly lists
Bulk match history for modelingget_matches by season with limit and offsetget_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_match returns serve_stats: null with a note that they are not published. Anything that needs serve percentage must be built from score proxies (see set-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_rank in search_players is null outside 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 AvailableUse Instead
get_oddsget_match and read its odds array
get_live_scoresget_schedule (status live) or get_matches with status: "live"
get_serve_statsNot available: no serve data exists in this dataset
get_player_by_namesearch_players with name
get_h2hget_head_to_head
get_eloget_ratings (leaderboard) or get_player (one player)
get_standingsget_rankings
get_injuriesNot 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. indoor is a separate flag on matches and tournaments, so indoor hard is surface: "hard" with indoor: 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

OperationCreditsNotes
Season of results, one tourabout 902026 had 45 get_matches pages at the time of writing (pricing page)
Odds for every covered match in a seasonabout 7,1483,574 get_match calls for 2026 (pricing page)
Preview of one match15 to 20see match-preview
Player scouting report8 to 16see 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 DoWhy It's WrongDo This Instead
Guess a player id from a nameIds are exact strings, and the WTA has its ownsearch_players first, with league if WTA
Read rating.after as the pre-match ratingIt contains the match result (leakage)Use rating.before or rating_before
Subtract a surface rating from an overall ratingThey are on different scales (fewer matches, different K)Compare like with like: surface to surface
Treat a null rank as rank 0It means outside the top 100 to 150Handle as unranked
Count a walkover as a played matchNo sets were playedFilter outcome == "completed"
Loop get_player_matches for a dataset5 credits per 50 matchesget_matches by season, 2 credits per 100

What to Do Next

What You RetrievedNext ActionSkill
A day of resultsSummarize, find upsetsmatch-lookup
A player profileScouting reportplayer-scouting
Two player idsMatchup briefmatch-preview
Surface ratingsWin probabilitysurface-elo
odds arrayRemove the marginodds-analysis
A season of matchesBuild featuresfeature-engineering

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