Fan Token Intel

Fan-token intelligence for Chiliz Chain: prices, whale flows, match event impact.

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Fan-token intelligence for Chiliz Chain: prices, whale flows, match event impact. 22 read tools.

Список инструментов сервера (25)

Технические названия из tools/list. Нужны только разработчикам.

tokenintel_discover	Discover available tools on the Fan Token Intel MCP server. Returns tool names and one-line descriptions, organized by category. Call with no arguments for all categories, or specify a category to filter. Categories: market_data, signals, sports, social, defi, agent, portfolio, volume, chain_info. Tip: connect with ?modules=market_data,signals to load only specific categories.
tokenintel_describe	Get the full input schema for a specific tool. Returns the JSON Schema (parameters, types, required fields, descriptions) needed to call the tool via tokenintel_invoke. Use tokenintel_discover first to find tool names.
tokenintel_invoke	Invoke any tool on the Fan Token Intel MCP server by name. Pass the tool_name and its arguments. The result is identical to calling the tool directly. Auth and rate limits apply as normal. Use tokenintel_describe to get the required arguments first.
tokenintel_register	Get a free Fan Token Intel API key, self-serve — no human in the loop. Provide a name, an email, and terms_accepted=true; the key (ti_live_...) comes back in the response along with your tier and rate limits. Free-tier keys unlock the read-only descriptive data layer (60 req/min); premium event-impact tools stay metered via x402. Pass the key as 'Authorization: Bearer ti_live_xxx' (HTTP) or the TOKENINTEL_API_KEY env var (stdio). Rate-limited to one registration per 10 minutes per caller.
tokenintel_briefing	All-in-one ECOSYSTEM briefing: market regime, active signals, anomalies, health matrix, sports calendar, and whale activity in one response. Use instead of calling 6+ tools sequentially. USE THIS for a market-wide overview. USE tokenintel_token_context for a SINGLE-TOKEN deep dive (price, signals, health, whale flow, sports catalyst, news — all for one symbol). Returns data, not recommendations -- interpret results yourself.
tokenintel_health_matrix	Get health grades (A-F) for all tracked fan tokens. Each token is scored across trading volume, order-book liquidity, spread, holder distribution and price stability -- 5-pillar weighted: volume(25%) + liquidity(25%) + spread(20%) + holders(15%) + price_stability(15%). The grade is the token's percentile standing WITHIN the fan-token universe (A=top 10%, B=next 20%, C=middle 40%, D=next 20%, F=bottom 10%); health_score stays the absolute 0-100 pillar score. A pillar whose collector delivered no data is excluded and the remaining weights are renormalized (see missing_pillars), never scored as 0. Use this to quickly filter which tokens deserve attention relative to their peers. Detailed mode (default) includes the per-pillar sub-scores; pass response_format='concise' to get just symbol/grade/score/change (~70% smaller) when you don't need team/league/volume/age detail.
tokenintel_market_regime	Get current market conditions — BTC trend, CHZ momentum, fear/greed index, and the platform's market regime classification. Useful for filtering or adjusting signal confidence based on macro conditions.
tokenintel_macro_context	Get current crypto macro context: BTC dominance, CHZ price, funding rates, fear & greed index, and risk environment assessment.
tokenintel_token_context	SINGLE-TOKEN deep dive: realtime price, CEX whale flow, on-chain Chiliz Chain (FanX) liquidity with slippage at 1%/5% of reserves, and upcoming matches for one symbol. The default tool to call before evaluating a trading decision on a specific token. USE THIS when you have a target token in mind. USE tokenintel_briefing when you want the market-wide overview instead.
tokenintel_realtime_prices	Get the freshest available prices with staleness metadata. Returns price_age_seconds so agents know exactly how stale each price is. Lightweight and fast -- call this before any trade decision to get current prices. Supports multiple tokens in a single call.
tokenintel_price_candles	Historical OHLCV price candles for any fan token. Intervals: 1h, 4h, 1d. Up to 180 days lookback. Returns open, high, low, close, volume for each period. Use for backtesting, charting, trend analysis, or building your own signals.
tokenintel_whale_flows	Get real-time whale distribution data for a fan token. Shows the ratio of whale sells to total whale activity on CEX exchanges. sell_ratio = whale sell volume / total whale volume; the payload labels >0.65 'distribution' and <0.35 'accumulation' (descriptive labels, not a signal). Data aggregated from CEX exchanges in real-time. USE THIS for aggregate buy/sell pressure on CEX. USE tokenintel_whale_trades for individual trade rows. USE tokenintel_dex_whales for on-chain (Chiliz Chain) swap whales.
tokenintel_social_sentiment	Social sentiment for a fan token. The only live social source is the LunarCrush aggregated feed, and on the current plan it provides galaxy_score and alt_rank ONLY — sentiment and social_volume come back null (see lunarcrush.fields_unavailable); they are unavailable, not zero. Native X/Twitter ingestion was retired 2026-03-01 and native Reddit/YouTube ingestion has never run in production, so those blocks read 0 — data_sources labels each pipeline (active / no_recent_data / inactive_since_<date> / never_active). overall_sentiment is computed only from sources that actually reported activity and is null when none did. Descriptive community-mood data, not a recommendation.
tokenintel_capital_rotation	Cross-token capital flow analysis. Shows which fan tokens are gaining vs losing volume relative to their recent average. Detects rotation: when whales exit one token, where does the capital go?
tokenintel_match_impact_history	Historical match price impact data for a fan token. Returns price snapshots at -24h, kickoff, fulltime, +1h, +24h with returns for each match. Filter by result (win/loss/draw), competition, venue. WINDOWS: return_total_pct is measured price_24h_before -> price_24h_after (it includes the pregame move, so it can differ in sign from a kickoff-anchored return); return_ko_to_24h_pct is kickoff -> +24h. See the 'semantics' block in the response. Use for backtesting sports-driven strategies.
tokenintel_match_correlation	Historical match-to-price correlation. Ask 'what happens to BAR after Champions League wins?' and get backtested data with price impact percentages. Returns individual match records with price at kickoff, fulltime, +1h, +24h and aggregate stats (avg impact, win rate, best/worst).
tokenintel_goal_direction_asymmetry	THE event-impact moat: how a fan token reacts when its team SCORES vs CONCEDES a goal, market-adjusted vs CHZ at +15/+30/+60m. The blended 'all goals' number hides the real signal — scoring is ~priced-in, conceding moves price. Returns the scored-vs-conceded decomposition with sample sizes and directional hit rates. Only computable here (needs the token<->team map). Descriptive history, not advice.
tokenintel_event_reaction_profile	Event-conditioned, market-adjusted (vs CHZ) token reaction profiles for football events — by event_type x event_side(for/against) x minute x scoreline_state x importance. Returns mean/median abnormal return, match-clustered t-stat, bootstrap 95% CI, hit rate, decay/persistence, n_events, n_matches, FDR. Omit a dimension to pool. Every cell carries its sample size — descriptive history, not advice.
tokenintel_late_game_redcard_profile	Red-card reaction profile (market-adjusted vs CHZ). Rare and high-impact: returns abnormal return at +15/+30/+60m with honest wide confidence intervals and sample size; flags cells with n<15. Descriptive history, not advice.
tokenintel_match_event_replay	Event-by-event reaction tape for a single match: each goal/red card with its minute, running score, scoreline state, and the market-adjusted token reaction at +15/+30/+60m (plus pre-event drift). The non-reconstructable moat artifact. match_id selects that fixture; token (+ optional date) resolves ONE fixture (the date-selected or most recent) and returns its tape, match metadata, and an other_matches index. Events are never merged across fixtures.
tokenintel_match_odds	Prediction-market odds curve for a single match from the in-play odds tape (odds_ticks): per-market (home/draw/away) implied-probability series with source labels (polymarket = CLOB midpoint, apifootball = de-vigged bookmaker odds), pre-match vs in-play segmentation against kickoff, and open/close/min/max summary stats per market. Settlement wind-down artifacts (ticks after a market first prints prob >= 0.99, or after full-time +15min) are excluded by default and counted via excluded_settlement_ticks. Curves are downsampled to <=300 points per market (labeled). match_id selects a fixture directly; token (+ optional date) resolves the most recent covered fixture. Use tokenintel_odds_coverage to discover which matches have odds data.
tokenintel_odds_coverage	Discover which matches have prediction-market odds coverage in the in-play odds tape (odds_ticks): per-match tick counts by source (polymarket = CLOB midpoint, apifootball = de-vigged bookmaker odds), in-play tick counts vs kickoff, capture span, live dataset totals (computed from the table, never hardcoded), and upcoming fixtures already mapped for capture. In-play odds are unbackfillable — a match that passed uncaptured stays uncovered. Use tokenintel_match_odds to fetch a covered match's probability curves.
tokenintel_governance_validators	List active validators on Chiliz Chain governance. Shows validator addresses and total CHZ delegated to each. Use this to find the best validator before staking.
tokenintel_dex_depth	Get DEX depth and slippage curves for fan token pools on Chiliz Chain. Computes constant-product (x*y=k) price impact at trade sizes [1%, 5%, 10%, 25%] of pool reserves. Useful for agents evaluating execution costs before trading. Data from latest on-chain liquidity snapshots.
tokenintel_dex_liquidity	Get on-chain DEX liquidity data for fan tokens on Chiliz Chain. Returns pool TVL, depth, token reserves, and estimated slippage. Critical for agents that want to understand execution costs before trading on-chain.