Propline MCP vs Stathead MCP
A side-by-side look at the Propline MCP and Stathead MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Propline MCP for sports work as a local process; choose Stathead MCP for sports as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Propline MCP when
- You need dedicated capabilities in the Sports domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
PROPLINE_API_KEY. - You want tools like
propline_list_sports, propline_list_events, propline_list_event_markets.
Choose Stathead MCP when
- You need dedicated capabilities in the Sports domain.
- You prefer a local stdio subprocess transport.
- You want tools like
get_metadata, get_player_season_stats, get_player_weekly_stats.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Sports | Sports |
| Quality signal | 64/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | PROPLINE_API_KEY | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 2 |
| Directory activity | 2 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 34 | 55 |
| Full listing | View Propline MCP | View Stathead MCP |
Tools & capabilities
Propline MCP tools 34
propline_list_sportsList all sports PropLine currently polls. Returns sport keys (e.g. baseball_mlb, basketball_nba, soccer_epl) along with human titles and active status. Use this first to discover what sport_key values are valid for the other tools.propline_list_eventsList upcoming events for a sport. Returns each event's id, home_team, away_team, commence_time. Use the returned event_id to drill into per-event odds, props, +EV, or results.propline_list_event_marketsList the market types available for a specific event (e.g. h2h, spreads, totals, player_points, pitcher_strikeouts). Useful when you don't know which prop markets a given event carries.propline_get_oddsGet live odds. If event_id is supplied, returns full per-event props for that event; otherwise returns bulk game-line odds for the whole sport. Pass markets as a comma-separated list (e.g. 'h2h,spreads,totals' or 'player_points,player_rebounds'). Response includes a bookmakers[] array across every book that carries the requested markets (currently up to 37 — sportsbooks: Bovada, DraftKings, FanDuel, Pinnacle, BetMGM, BetRivers, Unibet, Betway, Hard Rock Bet, Fanatics, BetUS, BetOnline.ag, LowVig.ag, 1xBet, TAB AU, ReBet, Fliff, Courtside, Sportzino, Heritage Sports, Jazz Sports, YouWager, Thrillzz; DFS: PrizePicks, Underdog, Sleeper, Dabble, ParlayPlay, DraftKings Pick6; exchanges and prediction markets: Kalshi, Polymarket, Polymarket US, Matchbook, Smarkets, Novig, ProphetX, SX Bet — coverage varies by sport). Every market carries line_type: 'main', 'alternate' or 'milestone' (an N+ rung such as '3+ Strikeouts'); filter to line_type == 'main' for each book's primary line. Kalshi and Polymarket US rows also set line_type on each outcome. Underdog Fantasy outcomes carry a payout_multiplier on EVERY outcome (1.0 = standard pick, e.g. 1.5 = boost, 0.75 = discount; null means the book is not Underdog) — keep only payout_multiplier == 1.0 when comparing DFS lines to sportsbook consensus, since filtering on non-null would drop every Underdog line. Each market carries suspended_at: null while on the board, set when that book pulled the market pregame (late scratch, dropped market type) — its outcomes are then the last quoted legs, not a live price. Treat a suspended market as unbettable and, if several books show it for one player, as a probable scratch. Each BOOKMAKER carries pregame_only: true when the event is live and that book does not price it in play, so its prices are the last pregame quote and will never move again this game. suspended_at cannot show this — a book with no in-play feed is never polled once the game starts, so nothing goes missing to flag. Exclude pregame_only books when reasoning about a live game; they are still returned because on DFS books that frozen line is what the bet settles against. Each market also carries team: the canonical event team name when the market is scoped to ONE team (a TEAM total), and null for the game total. Both ride the totals key, so NEVER compare totals on (market key, point) alone — a team total at 0.5 is not a game total at 0.5. Filter team == null for the game total; team matches home_team/away_team exactly. Always null outside totals. Each outcome carries side ('home'/'away'/'draw', null for Over/Under/Yes/No/player legs) so you can pair legs without matching team spellings. Each event carries is_outright (true for a tournament listing with no away side) and, on tennis, tournament + tour (ATP/WTA/Challenger/ITF/…).propline_get_odds_historyHobby+ endpoint. Returns the historical line-movement snapshot series for an event (every recorded price/point change per outcome over the event's lifetime). Free tier returns market structure with redacted snapshots and an upgrade pointer. Supports period-historical filters: from/to (absolute ISO), relative_from/relative_to (offsets to commence_time like '-3h' or '0'), interval downsample ('30s'/'1m'/'5m'/'15m'/'30m'/'1h'), and changes_only=true to drop unchanged adjacent snapshots.propline_get_odds_closingHobby+ endpoint. Returns the OPENING and CLOSING line per (book, market, outcome) for an event. Closing = the last snapshot at or before commence_time (price/point/closing_at); opening = the first snapshot PropLine holds for the outcome, however far before kickoff the book posted it (opening_price/opening_point/opening_at); pass opening_window to limit that lookback. Canonical CLV-tracking helper; one call returns both data points your bet should be measured against, instead of fetching full history and post-processing. Compare the POINTS as well as the prices — on spreads and totals the number moves as much as the price, so a price-only comparison mis-measures those markets. opening_age_seconds says how long before kickoff the opener was recorded: the archive starts 2026-04, so a small value means PropLine started polling late and this is not the book's true open. Free tier returns redacted structure with upgrade pointer.propline_grade_clvHobby+ endpoint. Grades PLACED bets against their closing lines. Closing line value is the only durable proxy for whether a bettor has edge: did the price they took beat the number the market settled on? Send the bets and each comes back with its closing price, the de-vigged closing fair probability, CLV, and — once the game settles — the graded resolution and actual stat value, plus a portfolio summary. Stateless: nothing is stored. TWO CLV numbers are returned deliberately. clv_pct is price-vs-price: familiar and quotable, but VIG-BLIND, so it flatters a bet taken on the juicy side of a wide market. ev_vs_close_pct scores the price against the DE-VIGGED close and is the honest one — report that one when the user asks whether they got value. The de-vig anchors to the SHARPEST book quoting that line at close (fair_source), not the book they bet at, because de-vigging their own book always returns a negative number (they paid its hold). Bets whose event has not started carry closing_is_final=false, are counted in summary.pending, and are EXCLUDED from the summary averages: before kickoff the 'closing' price is just the latest price, so CLV is ~0 by construction — do not present those as results. Matching is fail-closed: a bet that cannot be pinned to exactly one stored outcome returns matched=false with an unmatched_reason instead of a wrong match, so surface those rows rather than silently dropping them. Max 500 bets per request. Free tier returns structure with every number nulled.propline_price_sgpHobby+ endpoint. Prices a same-game parlay at the BOOK'S OWN correlated odds — the price a FanDuel customer would be offered for that exact slip right now, not a model of it. bookmaker is fanduel (default, its own pricer), draftkings (its SGP widget's pricer), betonlineag / lowvig (the Sportcast engine both share, same builder price), or 'all' to quote every book on the same legs in one call — then the response is {quotes, errors, best_bookmaker}, and best_bookmaker is the book charging the smallest correlation reduction (use 'all' whenever the user asks which book to bet an SGP at). Send 2-10 legs from ONE event, each named exactly as propline_get_odds names an outcome: market key, name (team / Over / Under / player for YES-only props), description (the player on a two-way prop, '' for game lines), point (omit for h2h and YES-only props), period (omit for full game) and team for a TEAM total (omit for the game total; a totals leg with no team matches the game total only) — or book_outcome_id from includeBookIds (on betonlineag / lowvig that is Sportcast's settlement id, e.g. MatchWinner_Home). The response carries sgp_price (the book's parlay price), independent_price (the product of the live single-leg prices) and correlation_factor = their ratio: below 1 the book is charging for correlation, above 1 it is paying for anti-correlation — say which when presenting it. Matching is fail-closed: a leg that does not pin to exactly one stored outcome is a 422 leg_unmatched naming the leg (an Over with no point on an event with two total lines is refused, not guessed) — fix the leg, do not retry blindly. quoted=false means the book will not offer that combination as an SGP; refused legs carry the book's own failure_code. Free tier returns the matched legs with every price nulled.propline_export_odds_historyBackfill-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.propline_get_futuresFree-tier endpoint. Returns season-long futures (outright) markets for a sport — championship/Super Bowl/division/conference winners, MVP and award winners, season win totals — aggregated across Bovada, FanDuel, DraftKings, Pinnacle, and Kalshi. One row per (futures event, book, market) with each team/player outcome and its price. Marquee markets (Super Bowl winner, MVP, division/conference) are quoted by multiple books for comparison; exotic markets are often single-book. Useful for: 'who are the Super Bowl favorites across books', 'NFL MVP odds', 'NBA championship futures'. Futures are unresolved (no settlement grade).propline_get_scoresFree-tier endpoint. Returns recent and live game scores plus status (scheduled, live, final) for a sport. Useful for: 'is this game over yet, what was the final score'.propline_get_dfs_payoutsFree-tier reference math. Returns the PrizePicks Power Play (all legs must hit) and Flex Play (partial payouts) entry payout schedule for 2-6 legs, plus the per-leg breakeven win probability for each play. Pass leg_win_prob (e.g. 0.58) to also get expected_return (per $1) and is_plus_ev per play — the slip-level breakeven. Useful for: 'what hit rate do I need to beat a 4-pick PrizePicks Power play', 'is a 3-leg flex +EV at 60% per leg'. NOTE: standard published payouts only — demon/goblin per-pick modifiers aren't in PrizePicks's feed (see the disclaimer field); breakeven assumes independent legs.- +22 more on the listing page
Stathead MCP tools 55
get_metadataDescribe this server's capabilities: available tools, data sources, per-source season coverage, and valid enum values (positions, scoring formats, league sizes, ADP sources). Call this FIRST to scope a question correctly — it tells you what seasons/sources/formats are supported so you don't have to probe by trial and error. Takes no arguments.get_player_season_statsGet aggregated season totals for NFL players — offence, defence (IDP), kicking and punting. Returns rushing, passing, receiving, fantasy stats and 2-point conversions, and via `fields` every other counting column nflverse carries: defensive (def_tackles_solo, def_tackle_assists, def_tackles_for_loss, def_sacks, def_qb_hits, def_pass_defended, def_interceptions, def_fumbles_forced, def_tds, def_safeties), punting (pt_att, pt_yards, pt_net_yards, pt_inside_20, pt_long, pt_touchback) and kicking (fg_made/fg_att by distance band, pat_made/pat_att). Filter by position: QB/RB/WR/TE/K/P/FB, the nflverse defensive codes (DE, DT, LB, CB, SAF), or the IDP buckets DL (DE+DT+NT), LB and DB (CB+SAF+FS+SS). Sort by any stat column. Punting columns need a season pulled from nflverse's unified table — 2025 onward locally, any season when fetched live. Use this for player performance, fantasy points, rankings, comparisons. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_player_weekly_statsGet week-by-week stats — game logs — for one player or for a whole position in a season. Pass player_name for one player's log, or omit it and pass position (and optionally week_start/week_end or team) for every player at that position, which is how you get a weekly board. Covers offence, defence (IDP) and specialists: with `fields` you can project any raw nflverse weekly column, including def_tackles_solo, def_tackle_assists, def_sacks, def_qb_hits, def_pass_defended, def_interceptions, def_tackles_for_loss, def_tds, punting (pt_att, pt_yards, pt_net_yards, pt_inside_20) and returns (punt_returns, punt_return_yards, kickoff_returns, kickoff_return_yards). Positions accept QB/RB/WR/TE/FB/K/P, the nflverse defensive codes (DE, DT, LB, CB, SAF) and the IDP buckets DL/LB/DB. A whole position-week is a few hundred rows, so narrow with week_start/week_end or team, and raise limit deliberately. Use for game logs, weekly trends, consistency and boom/bust analysis, and week-by-week IDP scoring. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_gamesGet NFL game results and schedules. Includes the canonical nflverse game_id (e.g. 2024_04_BUF_BAL, handles neutral-site/relocated games correctly), scheduled kickoff (gameday = date, weekday, gametime = kickoff in US Eastern HH:MM), home/away/week, scores, spreads, totals, weather, surface. Use for team records, point totals, home/away splits, deriving day/time windows, bye weeks (a team absent from a week's games is on bye), or to enumerate game_ids to feed get_play_by_play. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_snap_countsGet offensive/defensive snap count percentages by player per week. Use for workload analysis, role changes, snap share trends. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_rookie_snap_shareRookie snap-share RAMP tracker — weekly offensive snap % for a season's rookie class, with a ramp signal (last-3-week avg − first-3-week avg) that surfaces mid-season role expansion a static depth chart misses (the classic late-bloomer pattern, e.g. a rookie RB climbing from 30% to 70% snaps). Identifies rookies from the roster (years_exp 0 / rookie or entry year = season). For an in-progress season this is a live leading indicator; for a completed season it's the role-trajectory record. Sort by ramp (biggest risers), latest, peak, or avg. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_combine_resultsGet NFL Combine athletic testing results. Includes 40-yard dash, bench press, vertical jump, broad jump, 3-cone drill, shuttle, height, weight, draft position. Use for athletic profile analysis, draft capital evaluation. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_draft_picksGet historical NFL draft picks with career outcomes. Includes round, pick, team, college, career approximate value (car_av), Pro Bowls, All-Pro selections, Hall of Fame status. Note: career columns (car_av, pro_bowls, etc.) are cumulative and are blank/low for recent draftees who are still active — they populate as careers progress. Use for draft analysis, career success by pick, team drafting history. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_rookie_classOne-call rookie-class join: NFL draft picks for a draft year + combine testing + that player's rookie-season (same year) production — so you don't have to join draft, combine, and stats by hand. Skill positions (QB/RB/WR/TE) by default. Each row carries pfr_id for joining to other tools. Great for prospect/dynasty evaluation and hit-rate analysis. Anti-pattern: do not multiply marginal factors (athleticism × age × draft slot) into a "P(Hit)" — the factors are correlated and the product is over-confident; use as ordinal ranking or compare similar-player (joint) comps instead. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_injuriesGet weekly NFL injury reports (nflverse, 2009+). Per player-week: gsis_id (joinable to get_play_by_play / get_player_crosswalk), primary + secondary injury, practice participation (primary/secondary injury + practice_status), and final game report_status (Out/Doubtful/Questionable). Use for injury impact analysis, weekly availability, and attaching injury status to a roster. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_advanced_statsGet PFR advanced stats: passing (pressure rate, drop rate, bad throws), rushing (yards before/after contact, broken tackles), receiving (drops, YAC). Use for efficiency and process metrics beyond box score. See also: get_metadata for season coverage, valid enums, and analytic caveats.get_play_by_playGet play-by-play data with EPA, WPA, win probability, air yards, YAC, and stable player IDs (passer/rusher/receiver_player_id = nflverse gsis_id, joinable to get_player_crosswalk). Default view is lean; pass `fields` to project any of these slim columns — including kicker/defense/special-teams columns for K & DST fantasy scoring: kicker_player_id, kicker_player_name, kick_distance, extra_point_result; sack, interception, fumble, fumble_lost, fumble_recovery_1_team, safety; td_player_id, td_team, return_touchdown (separates defensive/ST TDs from offensive); two_point_attempt, two_point_conv_result; and kick/punt return columns: return_yards, return_team, kickoff_returner_player_id, kickoff_returner_player_name, punt_returner_player_id, punt_returner_player_name, fair_catch (note: yards_gained is 0 on return plays — use return_yards); committed-turnover + recovery attribution: interception (+ passer_player_id = who threw it), fumble_lost (+ fumbled_1_player_id/_name/fumbled_1_team = who lost it, fumble_recovery_1_player_id/_name = who recovered, forced_fumble_player_1/2_player_id = who forced it); per-defender IDP attribution (project via fields): solo_tackle_1/2_player_id, assist_tackle_1..4_player_id, tackle_for_loss_1/2_player_id, sack_player_id, half_sack_1/2_player_id, qb_hit_1/2_player_id, interception_player_id, pass_defense_1/2_player_id (all gsis, crosswalkable; defensive/ST TD scorer is td_player_id with td_team=defteam); and real (wall-clock) time: game_date, start_time (kickoff), time_of_day (UTC time the play was run — vs the game-clock `time` field; note nflverse populates it for most recent-season plays but it can be sparse/older-season gaps). Use for situational analysis (red zone, 3rd down, 2-minute drill), play type breakdowns, EPA-based efficiency, scoring K/DST, or pulling a complete game with game_id. WARNING: Large dataset — always filter by game_id, player, team, or situation. To pull an ENTIRE WEEK set limit ~3000 (a week is ~2400 plays) with week=N; to pull a WHOLE SEASON set a high limit (e.g. limit=50000, season ~43k plays) — for these large pulls always use output_format=jsonl or csv (optionally + fields) to keep the payload compact. You can also page with offset (the header reports the next offset and the total). When projecting with `fields`, include game_id/play_id/posteam/defteam yourself if you need them for joins. See also: get_metadata for season coverage, valid enums, and analytic caveats.- +43 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Propline MCP
{
"mcpServers": {
"proplineapi-propline-mcp": {
"command": "npx",
"args": [
"-y",
"propline-mcp"
],
"env": {
"PROPLINE_API_KEY": "YOUR_PROPLINE_API_KEY_HERE"
}
}
}
}
Stathead MCP
{
"mcpServers": {
"stathead-mcp": {
"command": "npx",
"args": [
"-y",
"stathead-mcp"
]
}
}
}
Frequently asked questions
Propline MCP is categorized under Sports and uses a local stdio subprocess. Stathead MCP belongs to Sports and uses a local stdio subprocess. Pick Propline MCP for sports work and Stathead MCP for sports.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Propline MCP has no declared pricing — check the project's own site. Stathead MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add proplineapi-propline-mcp and stathead-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Propline MCP has a quality signal of 64/100 (Good) with 2 views, 0 installs and 1 GitHub stars. Stathead MCP has 63/100 (Good) with 1 views, 0 installs and 2 GitHub stars.