io.github.Ninjabeam20/sportiq-mcp
PYPI · SPORTIQ-MCP · 2 COMPONENTS · SCANNED SEP 24
MCP tools for FIFA World Cup 2026 football, Formula 1, and IPL cricket — sims, strategy, fantasy.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security32
- Malware scan not yet available for this package.Unverified
- CVE check failed: a known advisory affects this package; its severity couldn't be graded. See how to fix → View diagnostics → Fail
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- Dependency health was assessed across the 43 of 45 dependencies we could resolve, so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency48
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 21 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability82
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 7161 tokens (~159/item across 45 items; 44 tools + 1 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management73
- Stability observed for 22 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 44 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 45 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.Ninjabeam20/sportiq-mcp server?
io.github.Ninjabeam20/sportiq-mcp runs locally as a PyPI package, launched with uvx sportiq-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · sportiq-mcp
claude mcp add ninjabeam20-sportiq-mcp -- uvx sportiq-mcp
{
"mcpServers": {
"ninjabeam20-sportiq-mcp": {
"command": "uvx",
"args": [
"sportiq-mcp"
]
}
}
} {
"servers": {
"ninjabeam20-sportiq-mcp": {
"command": "uvx",
"args": [
"sportiq-mcp"
]
}
}
} codex mcp add ninjabeam20-sportiq-mcp -- uvx sportiq-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"ninjabeam20-sportiq-mcp": {
"type": "local",
"command": [
"uvx",
"sportiq-mcp"
],
"enabled": true
}
}
} openclaw mcp add ninjabeam20-sportiq-mcp --command uvx --arg sportiq-mcp
mcp_servers:
ninjabeam20-sportiq-mcp:
command: "uvx"
args: ["sportiq-mcp"] {
"McpServers": {
"ninjabeam20-sportiq-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"sportiq-mcp"
]
}
}
} assistant mcp add ninjabeam20-sportiq-mcp -t stdio -c uvx -a sportiq-mcp
{
"mcpServers": {
"ninjabeam20-sportiq-mcp": {
"command": "uvx",
"args": [
"sportiq-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 23 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.
- 21 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 16 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.
- 15 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- 14 Sept 26 −14
- Malware scan: pass → unverified ▼ security
- 12 Sept 26 +16
- Malware scan: unverified → pass ▲ security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 24 Sept 2026 · Analysed pypi/sportiq-mcp@0.3.2
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Vulnerabilities 2 findings
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-w8v5-vhqr-4h9v | CVE-2025-69872 | medium | no | |
| PYSEC-2026-2447 | CVE-2025-69872 | none | no |
Background: What a vulnerability scan can and cannot prove →
Dependencies 43 packages
| Packages resolved | 43 |
|---|---|
| Stale | 2 |
| Tree resolution | Partial |
The dependency tree was only partially resolved, so these counts may be incomplete.
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
cricket_build_dream11_team ~388
Recommend an optimal fantasy XI + captain + vice-captain for one fixture. Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name (e.g. ``MI``). Required if match_id is absent. team_b: Second team code/name (e.g. ``CSK``). Required if match_id is absent. venue: Venue key/name (e.g. ``wankhede``). Required if match_id is absent. strategy: ``"balanced"`` only in Phase 2; future variants reserved. Returns: data.players: 11 picked players with name/role/credits/team/projected_points. data.captain: name of the chosen captain. data.vice_captain: name of the chosen VC. data.total_credits: sum of credits used (<= 100). data.total_projected_points: fantasy points including C x2 and VC x1.5 boosts. meta.estimated: true — projections are model output, not a fantasy oracle. Example: cricket_build_dream11_team(team_a="MI", team_b="CSK", venue="wankhede") cricket_build_dream11_team(match_id="abc123")
| Name | Type | Req | Description |
|---|---|---|---|
| match_id | – | – | CricAPI match identifier; resolves team_a/team_b/venue automatically. |
| strategy | string | – | ``"balanced"`` only in Phase 2; future variants reserved. |
| team_a | – | – | First team code/name (e.g. ``MI``). Required if match_id is absent. |
| team_b | – | – | Second team code/name (e.g. ``CSK``). Required if match_id is absent. |
| venue | – | – | Venue key/name (e.g. ``wankhede``). Required if match_id is absent. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_captain_recommendation ~276
Return the top-3 captain candidates ranked by projected points. IPL venues only (pitch seed is IPL grounds). Test/international matches and unknown venues fail rather than inventing a ranking. Same-role players often tie: projections use default form 55 and default opposition 0.5, not per-player history. Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name (IPL ground, e.g. ``wankhede``). Required if match_id is absent. Returns: data.candidates: list of 3 dicts with name/role/team/projected_points. meta.source: model:captain_score. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| match_id | – | – | CricAPI match identifier; resolves team_a/team_b/venue automatically. |
| team_a | – | – | First team code/name. Required if match_id is absent. |
| team_b | – | – | Second team code/name. Required if match_id is absent. |
| venue | – | – | Venue key/name (IPL ground, e.g. ``wankhede``). Required if match_id is absent. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_differential_picks ~269
Suggest low-ownership picks with positive projected upside. Ownership is *estimated* — proxied by credit weight (lower-credit players tend to have lower ownership), not real ownership data. Flagged ``estimated: true`` in the response. Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name. Required if match_id is absent. ownership_threshold: percent ownership cap; affects estimated label. Returns: data.picks: list of {name, role, team, credits, projected_points, estimated_ownership_pct}. meta.source: model:captain_score (filtered). meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| match_id | – | – | CricAPI match identifier; resolves team_a/team_b/venue automatically. |
| ownership_threshold | integer | – | percent ownership cap; affects estimated label. |
| team_a | – | – | First team code/name. Required if match_id is absent. |
| team_b | – | – | Second team code/name. Required if match_id is absent. |
| venue | – | – | Venue key/name. Required if match_id is absent. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_find_value_bets ~333
Compare model probabilities against market-implied IPL odds. Requires THEODDS_KEY. NOTE: cricket has no calibrated team-strength model wired yet (unlike the football Elo/Poisson path), so this tool currently returns an EMPTY ``value_bets`` list — scoring an edge against a neutral 50/50 prior would flag every market underdog, which would be misleading. It still reports how many events were screened so callers know odds were available. For raw de-vigged prices use ``cricket_get_live_odds``. Real edge detection lands when a cricket win model is wired (see cricket_head_to_head). Args: team: Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted). Returns: data.value_bets: always ``[]`` until a cricket model is wired. data.events_analysed: count of events screened (both teams present). data.model: ``"neutral_baseline"``. data.note: why no bets are emitted. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| min_edge | number | – | Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted). |
| team | – | – | Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_live_matches ~63
Return all currently live cricket matches across all series. Returns: data.matches: list of live match objects (team names, score, status). meta.source: which adapter served the response. meta.is_stale: true if data is from stale cache.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_live_odds ~303
Return live market head-to-head odds for upcoming/live IPL matches. IPL only (~March-May). An empty ``events`` list outside that window is a successful empty market, not an outage. Not international/Test/other T20 leagues. For World Cup 2026 football odds use ``football_get_odds``. Sourced from The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every IPL event. The Odds API uses its own opaque event ids, so a CricAPI match_id cannot be resolved to an event yet — filtering is by team name. Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, away}]} with decimal h2h prices per bookmaker. Empty when no IPL events are listed (typical off-season). meta.source: adapter that served the data (theodds / cache:stale).
| Name | Type | Req | Description |
|---|---|---|---|
| team | – | – | Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every IPL event. The Odds API uses its own opaque event ids, so a CricAPI match_id cannot… |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_pitch_report ~105
Summarise pitch characteristics for a venue. Args: venue: Venue key (e.g. ``wankhede``), official name, or city. Returns: data: {batting_friendly 0..1, expected_first_inn, recommendation, venue, pitch_type}. meta.source: which adapter served the venue record.
| Name | Type | Req | Description |
|---|---|---|---|
| venue | string | yes | Venue key (e.g. ``wankhede``), official name, or city. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_points_table ~100
Return the points table / standings for a cricket series. Args: series_id: The series identifier (e.g. IPL 2026 series ID from CricAPI). Returns: data: points table rows with team, P, W, L, NRR, Points. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | string | yes | The series identifier (e.g. IPL 2026 series ID from CricAPI). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_schedule ~189
Return the upcoming match schedule, optionally filtered by series. Args: series_id: Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series. limit: Max matches to return, 1..200 (default 50). offset: Number of matches to skip for paging (default 0). Returns: data.matches: page of upcoming matches with teams, date, venue. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max matches to return, 1..200 (default 50). |
| offset | integer | – | Number of matches to skip for paging (default 0). |
| series_id | – | – | Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_scorecard ~85
Return the full scorecard for a specific match. Args: match_id: The match identifier (e.g. from cricket_get_live_matches). Returns: data: full scorecard with innings, partnerships, bowling figures. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| match_id | string | yes | The match identifier (e.g. from cricket_get_live_matches). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_get_squad ~166
Return the squad roster for a cricket team, optionally for a specific series. Args: team: Team code or name (e.g. "MI", "CSK", "IND", "AUS"). series_id: Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data. Returns: data.players: list of players with name, role, and credits. meta.source: adapter that served the data (cricapi / static_seed).
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | – | – | Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data. |
| team | string | yes | Team code or name (e.g. "MI", "CSK", "IND", "AUS"). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_head_to_head ~171
Compare two cricket teams head-to-head using squad form and player stats. Args: team_a: First team code or name (e.g. "MI", "India"). team_b: Second team code or name (e.g. "CSK", "Australia"). Returns: data: {team_a, team_b, team_a_edge_count, team_b_edge_count, key_players_a, key_players_b, h2h_win_rate_a, h2h_win_rate_b, win_prob_a, win_prob_b}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| team_a | string | yes | First team code or name (e.g. "MI", "India"). |
| team_b | string | yes | Second team code or name (e.g. "CSK", "Australia"). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_player_form_index ~126
Report a 0-100 form score for a player using the player_stats chain. Args: player_id: Upstream player identifier (CricAPI/Cricbuzz id). Returns: data.form_score: 0..100 indicator. data.trend: "rising" / "stable" / "falling". data.samples: how many recent innings were available. meta.source: which adapter served the underlying stats. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| player_id | string | yes | Upstream player identifier (CricAPI/Cricbuzz id). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cricket_player_matchup ~130
Analyse the head-to-head matchup between two cricket players based on role and career stats. Args: player_a: Player ID or name for the first player. player_b: Player ID or name for the second player. Returns: data: {matchup_type, edge_holder, edge_reason, signals, role_a, role_b}. meta.estimated: true — heuristic model, not ball-by-ball H2H data.
| Name | Type | Req | Description |
|---|---|---|---|
| player_a | string | yes | Player ID or name for the first player. |
| player_b | string | yes | Player ID or name for the second player. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
cross_sport_build_accumulator ~122
Model the joint probability of multiple outcomes across football and cricket. Args: legs: Total legs across both sports (2-8). Default 3. min_edge: Minimum edge per leg. Default 0.05. Returns: data: same shape as football_build_accumulator, with sport field per leg. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| legs | integer | – | Total legs across both sports (2-8). Default 3. |
| min_edge | number | – | Minimum edge per leg. Default 0.05. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_drivers ~76
Return driver list for a specific F1 session. Args: session_key: OpenF1 session identifier. Returns: data.drivers: list of driver objects with driver_number, full_name, team. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_lap_times ~240
Return lap times for a driver in a specific F1 session. Args: session_key: OpenF1 session identifier. driver_number: Driver's race number (e.g. 1 for Verstappen). limit: Max laps to return, 1..200 (default 100 — covers most full races). offset: Number of laps to skip for paging (default 0). Returns: data.laps: page of lap objects with lap_number and lap_duration. OpenF1 does not put compound/tyre_life here — those live on the stints endpoint. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| driver_number | integer | yes | Driver's race number (e.g. 1 for Verstappen). |
| limit | integer | – | Max laps to return, 1..200 (default 100 — covers most full races). |
| offset | integer | – | Number of laps to skip for paging (default 0). |
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_race_results ~148
Return the final classification for one F1 race, keyed by year and round. Args: year: Championship year (e.g. 2025). round: Round number within the season (1-based; e.g. 1 for the opener). Returns: data.results: Ergast/Jolpica RaceTable payload — finishing order, times, grid positions, points, and fastest laps for the race. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| round | integer | yes | Round number within the season (1-based; e.g. 1 for the opener). |
| year | integer | yes | Championship year (e.g. 2025). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_sessions ~117
Return F1 sessions for a given year, optionally filtered by country. Args: year: Championship year (e.g. 2025). country: Optional country name to filter (e.g. "Monaco"). Returns: data.sessions: list of session objects with session_key, session_type, date. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| country | – | – | Optional country name to filter (e.g. "Monaco"). |
| year | integer | yes | Championship year (e.g. 2025). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_standings ~137
Return F1 driver and constructor championship standings for a year. Use this for "who is leading / who will win the F1 championship this year". There is no F1 title Monte Carlo — current points and position are the answer. This is not a cricket or football tool. Args: year: Championship year (e.g. 2026). Returns: data.driver_standings: driver championship positions and points. data.constructor_standings: constructor championship positions and points. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| year | integer | yes | Championship year (e.g. 2026). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_get_weather ~72
Return weather data for a specific F1 session. Args: session_key: OpenF1 session identifier. Returns: data.weather: list of weather snapshots with temperature, rainfall, wind. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_head_to_head_pace ~121
Compare lap-time pace distribution between two drivers in a session. Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number. Returns: data: {driver_a_avg_s, driver_b_avg_s, delta_s, faster_driver}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| driver_a | integer | yes | First driver's race number. |
| driver_b | integer | yes | Second driver's race number. |
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_predict_pit_strategy ~378
Predict the optimal pit-stop strategy for a driver in an F1 race session. Args: session_key: OpenF1 session identifier for a recorded race. driver_number: Driver's race number (e.g. 1 for Verstappen). current_lap: Current lap to project from (default 1 = full race ahead). total_laps: Total race laps. If omitted, inferred from the highest observed lap_number in the fetched laps (correct for Monaco 78 / Spa 44), falling back to 57 when no laps are available. An explicit value always wins. Returns: data.stop_laps: recommended pit laps. data.compound_sequence: tyre compounds for each stint. data.expected_finish_position: currently always None (not modelled). data.confidence: 0.0-1.0 model confidence. meta.total_laps: race length used (explicit arg, else inferred from laps). meta.estimated: true. Example: f1_predict_pit_strategy(session_key=9158, driver_number=1) f1_predict_pit_strategy(session_key=9158, driver_number=16, current_lap=20, total_laps=78)
| Name | Type | Req | Description |
|---|---|---|---|
| current_lap | integer | – | Current lap to project from (default 1 = full race ahead). |
| driver_number | integer | yes | Driver's race number (e.g. 1 for Verstappen). |
| session_key | integer | yes | OpenF1 session identifier for a recorded race. |
| total_laps | – | – | Total race laps. If omitted, inferred from the highest observed lap_number in the fetched laps (correct for Monaco 78 / Spa 44), falling back to 57 when no laps are available. An explicit value alway… |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_qualifying_analysis ~135
Analyse a qualifying session: best lap per driver, gap to pole, projected grid. Args: session_key: OpenF1 session identifier for a Qualifying session. Returns: data.grid: [{position, driver_number, full_name, team_name, best_lap_gap_s}]. data.pole_time_s: pole lap duration in seconds. data.drivers_analysed: count of drivers with valid laps. meta.estimated: true — grid derived from session laps, not official timing.
| Name | Type | Req | Description |
|---|---|---|---|
| session_key | integer | yes | OpenF1 session identifier for a Qualifying session. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_race_pace_compare ~129
Compare race-pace and tyre degradation between two F1 drivers in a session. Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number. Returns: data: {by_compound, overall_faster, compounds_compared}. meta.estimated: true — degradation model fit, not official timing.
| Name | Type | Req | Description |
|---|---|---|---|
| driver_a | integer | yes | First driver's race number. |
| driver_b | integer | yes | Second driver's race number. |
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_tyre_degradation ~146
Fit a tyre degradation model for a driver + compound in a session. Args: session_key: OpenF1 session identifier. driver_number: Driver's race number. compound: Tyre compound (SOFT, MEDIUM, HARD, INTER, WET). Returns: data: {intercept, slope, residual_std, sample_count}. meta.estimated: true — model output, not telemetry oracle.
| Name | Type | Req | Description |
|---|---|---|---|
| compound | string | yes | Tyre compound (SOFT, MEDIUM, HARD, INTER, WET). |
| driver_number | integer | yes | Driver's race number. |
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_undercut_window ~144
Estimate whether an undercut is viable for the attacker against the target. Args: session_key: OpenF1 session identifier. attacker_number: Attacking driver's race number. target_number: Target driver's race number. current_lap: Current lap number in the race. Returns: data: {laps_to_clear, viable, marginal}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| attacker_number | integer | yes | Attacking driver's race number. |
| current_lap | integer | yes | Current lap number in the race. |
| session_key | integer | yes | OpenF1 session identifier. |
| target_number | integer | yes | Target driver's race number. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
f1_weather_strategy_impact ~77
Analyse weather data and recommend compound or pit-window adjustments. Args: session_key: OpenF1 session identifier. Returns: data: {has_rain, avg_track_temp_c, compound_recommendation, recommendation}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| session_key | integer | yes | OpenF1 session identifier. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_build_accumulator ~164
Model the joint probability of several match outcomes from the top model-vs-market gaps. Calls ``football_find_value_bets`` internally to fetch live odds, then selects the strongest legs and combines them under the joint-probability model. Args: legs: Number of legs (2-8). Default 3. min_edge: Minimum edge threshold per leg. Default 0.05. Returns: data: {legs, legs_used, combined_odds, combined_model_prob, combined_edge, risk_flag, independence_warning}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| legs | integer | – | Number of legs (2-8). Default 3. |
| min_edge | number | – | Minimum edge threshold per leg. Default 0.05. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_find_value_bets ~327
Surface the largest gaps between the model's win probability and the market. De-vigs each market's 1X2 decimal odds (removes the margin so implied probabilities sum to 1) and compares them to this server's own match-outcome probabilities — the same Elo/Poisson path ``football_match_predictor`` uses. Where the model probability exceeds the de-vigged market probability by at least ``min_edge``, the outcome is flagged with its edge and the model's fair odds. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to scan every WC 2026 odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points). Returns: data.value_bets: list of {event_id, home, away, outcome, model_prob, fair_odds, market_odds, edge, bookmaker}, sorted by edge descending. data.events_analysed: events with both teams rated (model-comparable). meta.estimated: true. meta.is_stale reflects the odds freshness.
| Name | Type | Req | Description |
|---|---|---|---|
| min_edge | number | – | Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points). |
| team | – | – | Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to scan every WC 2026 odds event. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_form_trends ~118
Return rolling form, goal record, and xG trend for a football team. Args: team: Team name (e.g. "Brazil", "Argentina"). Returns: data: {form_string, wins, draws, losses, goals_scored, goals_conceded, xg_for, xg_against, recent_trend, matches_analysed}. meta.estimated: true — derived from available fixture data.
| Name | Type | Req | Description |
|---|---|---|---|
| team | string | yes | Team name (e.g. "Brazil", "Argentina"). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_fixtures ~162
Return World Cup 2026 fixtures (live providers, else the group schedule). Args: limit: Max fixtures to return, 1..200 (default 50). offset: Number of fixtures to skip for paging (default 0). Returns: data.fixtures: page of {home, away, date/group, status, home_goals, away_goals}. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data (static_seed = group schedule only).
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max fixtures to return, 1..200 (default 50). |
| offset | integer | – | Number of fixtures to skip for paging (default 0). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_groups ~92
Return the FIFA World Cup 2026 group draw and advancement format. Returns: data.groups: {group_letter: [4 team codes]} for all 12 groups. data.format: 48-team / 12-group / top-2 + 8-best-thirds rule. data.teams: team-code -> {name, fifa_code} metadata. meta.source: adapter that served the data.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_match_stats ~130
Return a team's aggregate World Cup tournament statistics. Network-only enrichment: requires a configured API-Football (or football-data.org) key. There is no offline static fallback, so without a key the call returns a clean ALL_SOURCES_FAILED envelope. Args: team: API-Football numeric team id (not a country code). Returns: data.team_stats: {team, played, wins, goals_for, goals_against}. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| team | integer | yes | API-Football numeric team id (not a country code). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_odds ~178
Return live market head-to-head odds for upcoming World Cup 2026 matches. Sourced from The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing. Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every WC event. Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, draw, away}]} with decimal 1X2 prices per bookmaker. meta.source: adapter that served the data (theodds / cache:stale).
| Name | Type | Req | Description |
|---|---|---|---|
| team | – | – | Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every WC event. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_squad ~131
Return a national team's World Cup squad. Args: team: Team code or name (e.g. "ARG"). Without an API-Football key, the static seed serves an empty-but-valid squad (rosters are a follow-up). Returns: data.squad: list of {name, number, position, age}. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| team | string | yes | Team code or name (e.g. "ARG"). Without an API-Football key, the static seed serves an empty-but-valid squad (rosters are a follow-up). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_standings ~148
Return current World Cup 2026 group standings. Args: limit: Max standing rows to return, 1..200 (default 50). offset: Number of rows to skip for paging (default 0). Returns: data.standings: page of {rank, team, group, points, played, goals_diff}. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max standing rows to return, 1..200 (default 50). |
| offset | integer | – | Number of rows to skip for paging (default 0). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_get_top_scorers ~52
Return the World Cup 2026 top scorers. Returns: data.scorers: list of {name, team, goals, assists}. meta.source: adapter that served the data.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_knockout_path ~142
Round-by-round survival probabilities for one team in the full sim. Args: team: Team code (e.g. "FRA"). iterations: Number of tournament simulations (clamped to 100..20000). seed: Optional RNG seed. Returns: data: {team, reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| iterations | integer | – | Number of tournament simulations (clamped to 100..20000). |
| seed | – | – | Optional RNG seed. |
| team | string | yes | Team code (e.g. "FRA"). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_match_predictor ~123
Predict a single match: most likely scoreline + outcome probabilities. Args: home_team: First team code. away_team: Second team code. neutral: True for a neutral venue (World Cup default). Returns: data: {most_likely_score, home_win, draw, away_win, predicted_winner}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| away_team | string | yes | Second team code. |
| home_team | string | yes | First team code. |
| neutral | boolean | – | True for a neutral venue (World Cup default). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_simulate_bracket ~259
Monte Carlo the full World Cup 2026 — per-team round + title probabilities. Simulates all 12 groups, advances the top 2 + 8 best third-placed teams to a 32-team knockout, and plays it to a champion, ``iterations`` times. Args: iterations: Number of tournament simulations (clamped to 100..20000; ~10000 gives stable ±2% probabilities). seed: Optional RNG seed for reproducible output. Returns: data.teams: {code: {reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}} sorted by win probability descending. data.champion: most likely winner. data.iterations: iterations run. meta.estimated: true. meta.conditioned_matches: completed matches locked in (played group results fixed, decided knockout ties locked). Example: football_simulate_bracket() football_simulate_bracket(iterations=20000, seed=42)
| Name | Type | Req | Description |
|---|---|---|---|
| iterations | integer | – | Number of tournament simulations (clamped to 100..20000; ~10000 gives stable ±2% probabilities). |
| seed | – | – | Optional RNG seed for reproducible output. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_simulate_group ~140
Monte Carlo one group within the full 12-group qualification context. Args: group: Group letter A-L. iterations: Number of simulations (clamped to 100..20000). Returns: data.teams: Per-team position probabilities, p_auto_advance, p_best_third_advance, truthful combined p_advance, and avg_points. data.iterations: iterations actually run. meta.estimated: true. meta.conditioned_matches: completed matches locked in.
| Name | Type | Req | Description |
|---|---|---|---|
| group | string | yes | Group letter A-L. |
| iterations | integer | – | Number of simulations (clamped to 100..20000). |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
football_xg_model ~157
Estimate a match's expected goals and win/draw/loss probabilities. Args: home_team: First team code (e.g. "ARG"). away_team: Second team code (e.g. "BRA"). neutral: True for a neutral venue (no home advantage). World Cup default. Returns: data: {expected_home_goals, expected_away_goals, home_win, draw, away_win}. meta.estimated: true.
| Name | Type | Req | Description |
|---|---|---|---|
| away_team | string | yes | Second team code (e.g. "BRA"). |
| home_team | string | yes | First team code (e.g. "ARG"). |
| neutral | boolean | – | True for a neutral venue (no home advantage). World Cup default. |
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
sportiq_health ~55
Report cache backend, per-adapter healthcheck, and quota status. Returns: HealthReport-shaped dict with `cache_backend`, `cache_ok`, `adapters` (per-source ok/detail), and `quotas`.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| data | – | – | – |
| error | – | – | – |
| meta | – | – | – |
No examples provided.
What is the io.github.Ninjabeam20/sportiq-mcp server?
io.github.Ninjabeam20/sportiq-mcp is listed in the public MCP registry as io.github.Ninjabeam20/sportiq-mcp. MCP tools for FIFA World Cup 2026 football, Formula 1, and IPL cricket, sims, strategy, fantasy. This page covers its PyPI package (sportiq-mcp).
Is the io.github.Ninjabeam20/sportiq-mcp server safe to use?
io.github.Ninjabeam20/sportiq-mcp scores 62 out of 100 on VerifyMCP. We recorded 2 known advisories against it as of 24 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.Ninjabeam20/sportiq-mcp server expose?
io.github.Ninjabeam20/sportiq-mcp exposes 44 tools: sportiq_health, football_get_groups, football_get_fixtures, football_get_standings, football_get_squad, and 39 more. Their descriptions and schemas cost roughly 7,124 tokens of context every time the server is loaded.
Is the io.github.Ninjabeam20/sportiq-mcp server still maintained?
io.github.Ninjabeam20/sportiq-mcp is still listed as active in the MCP registry. We last reached this channel on 24 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.Ninjabeam20/sportiq-mcp server under?
io.github.Ninjabeam20/sportiq-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.