"MLB" matching MCP tools:

• query_eventsA

  Lumify Sports IntelligenceA

  Search events with a natural-language query instead of structured filters — e.g. 'live nfl games today' or 'college basketball this week'. Rule-based (not an LLM): recognizes sport (nfl/nba/mlb/nhl/tennis/soccer/ncaaf/ncaab + aliases like hockey, american football, college basketball), status (live/final/upcoming/…), dates (today/tomorrow, this week, next N days, YYYY-MM-DD ranges). Bare 'football' is ambiguous and left unrecognized. Response includes interpreted filters, equivalent REST call, and unrecognized_terms. Prefer list_events when you already know the structured filters you want.

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• get_oddsA

  Lumify Sports IntelligenceA

  Get current betting odds for an event: per-bookmaker lines and last-updated time. Includes in-play quotes while the event is underway; books that have not quoted since kickoff are omitted. bookmaker defaults to pinnacle. Use 'all' or a comma-separated list for multiple books — still 1 credit. Default is main lines (is_main=true); set include_alts for alternate spread/total rungs. Final events include result (won/lost/push/void) graded from the pre-kickoff close. MLB, tennis, and soccer (MLS + big-five) mains also include fair_price and consensus mirrored from published assessments. Returns available:false with no charge if odds aren't posted for this event yet. If bookmaker is omitted and Pinnacle hasn't posted a line yet (common for the first/last games of a preseason slate), falls back to the best-covered other book and adds requested_bookmaker='pinnacle' plus fallback_bookmaker to the response instead of reporting no odds; an explicit bookmaker='pinnacle' never falls back. Use get_odds_history for line movement over time.

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• recallA

  Mlb StatsA

  Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

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• get_quote_requirementsA

  My Insure Bank (MIB)A

  Returns the exact details to collect from the customer for a line of insurance — the same rating inputs MIB's own quote forms use. Call this FIRST, ask the customer the questions, then call compare_insurance_quotes with their answers. Omit product_type to see every line.

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• create_listingA

  PropertyList — Spanish Property MLSA

  Start a new property listing in YOUR agency's CRM from a description. Use when the agent is describing a property they have taken on and wants it in the system. Creates a DRAFT. It is never published and never appears on the portal from here: the agent adds photographs and the energy rating in the CRM and publishes it themselves. The response includes a direct link to the draft. Location is resolved against the real area tree, so a town, suburb or urbanisation name is enough. Requires an agency API key.

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• get_game_propsA

  flash-props-apiA

  Fetch all player props for one game identified by eventId. Read-only. No side effects. Requires an API key; rate-limited per your tier. Returns: { eventId, sport, homeTeam, awayTeam, startTime, props: Array<{ player, stat, line, overOdds, underOdds, bookCount, gameState?, flashProjection? }>, sources: string[], fetchedAt, delayed }. flashProjection is present when that sport + market has a registered Flash model and a player baseline is available; it is { value, sampleN, method, marketKey } and is never fabricated. overOdds and underOdds are American-format integers (e.g. -110, +115); null when odds are not available. The stats parameter filters to specific markets (e.g. "points,rebounds" for basketball, "strikeouts,hits_allowed" for MLB). Typical workflow: (1) call list_games to get eventIds, (2) call get_game_props with the eventId. Alternatively, call find_game with team names to resolve the eventId when you know the matchup. Event ids are prefixed ud- (Underdog Fantasy source) or bv- (Bovada source). Returns an error when the event id is not found, the game has ended with no active props, or lines have not been posted yet. When to use: when you have an eventId and want all props for that specific game. When not to use: use scan_props instead when you want a cross-game market view. Use find_player_props when you know the player name but not which game they are in.

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• search_withinA

  Mlb StatsA

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• verified_valuationA

  PropertyList — Spanish Property MLSA

  Get a valuation for a property or area from recorded market data, not asking prices. Returns the €/m² for the municipality and segment, an indicative valuation when you give a size (build_sqm) or a listing reference, and a content-hashed attestation URL the figure can be cited from. Check `verified` before citing. It is true ONLY for Spanish figures drawn from the notarial register (Consejo General del Notariado) with at least 10 recorded transactions behind them. Elsewhere - Portugal and Cyprus especially - the figure comes from a national house-price index and is an estimate: `transaction_backed` is false, `source_label` names the real source, and it must not be described as notary-verified. `indicative_band_pct` widens as the evidence thins. Only two segment attestations exist per municipality, so a request for penthouse or townhouse resolves to the nearest attested segment. `property_type` is the segment actually used and `requested_property_type` is what you asked for; when they differ the text says so. Also returns our proprietary network achieved-sale figure where available. If no attestation covers the area yet, it says so plainly (use area_market_summary for asking-price stats instead).

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• compare_insurance_quotesA

  My Insure Bank (MIB)A

  Get real-time premiums across MIB's partner insurers (NSIA, Heirs, Tangerine, Cornerstone, NEM, WellaHealth, emPLE…) in NGN. Health/travel/gadget offers include a buy_url. MOTOR offers deliberately have no link: after presenting motor quotes you MUST ask the customer in chat for their vehicle + address + next-of-kin details (each offer's to_buy field lists them) and then call get_buy_link — that is the only way to a motor checkout. Rating inputs: auto → sum_insured (vehicle value) + cover_type; health → plan_code + lives; travel → travellers + travel_class + travel_scope; gadget → sum_insured (device value) + device_type.

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• my_pipelineA

  PropertyList — Spanish Property MLSA

  List the cards on YOUR agency's CRM pipeline boards (Kanban), with each board's own stage names and card counts. Boards: lead, buyer, seller, tenant, nurture (each card is a contact) and property (each card is a listing). Stage names are whatever the agency renamed them to, so read them from the reply rather than assuming. Call with no pipeline for a summary of every board - use this to answer "what's in my pipeline?" or "how many buyers do I have?". Call with a pipeline for that board's cards, newest activity first. This is the board view. For enquiries that have just arrived and have not been worked yet, use my_leads instead. Returns the WHOLE agency's boards, not one agent's cards - an API key belongs to the agency, not to a person. Requires an agency API key; only ever returns the calling agency's own data.

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• deep_researchA

  Mlb StatsA

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• entity_profileA

  Mlb StatsA

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

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• scan_dependencyA

  Mlb StatsA

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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• bet_researchA

  Mlb StatsA

  Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

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• ai_visibility_checkA

  Mlb StatsA

  Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

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• get_todays_projectionsA

  olympus-bets-analyticsA

  Return today's free sports betting projections published by Olympus Bets Analytics. Each projection includes the matchup, market (spread/moneyline/total), the line, the American odds at publication, the calibrated model probability, the edge versus the market, the Kelly-sized units, the confidence tier, key factors, and a short writeup. These are PUBLIC projections — the same set published on https://app.olympus-bets.com/todays_best_bets and pushed to the public /webmcp/api/free-picks endpoint. Premium tier projections are not exposed here. Args: league: Optional league filter (e.g. "NBA", "NHL", "MLB", "CBB", "NFL", "SOCCER", "LOL", "GOLF"). Omit to return all leagues. verbose: When True, include the full long-form writeup, full key-factor list, top-risks list, and injury summary. Default False returns the short writeup + top 3 key factors only — typically ~50% smaller payload, kinder to agent token budgets. Set verbose=True when an agent specifically wants the detail (e.g., user asked "explain this pick"). Returns: ``{date, total, leagues_active, projections: [...]}``

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• get_model_vs_marketA

  olympus-bets-analyticsA

  Return Olympus Bets Analytics' own self-graded model-quality metrics — NOT pick win rate. This is a different question than "did our picks win money?" (see get_performance_summary / get_track_record for that). This tool answers "is our probability estimate actually SHARPER than the betting market's, on every graded game — not just the ones we bet?" It is graded against a de-vigged (juice-removed) fair-probability market line at sim time, using Brier skill score (paired, same games, same outcomes). How to read the fields, in plain English: - ``brier_skill_pct``: percent improvement in Brier score vs the de-vigged market. POSITIVE = our model is sharper than the market. NEGATIVE = the market is sharper than us. Most leagues are currently negative — that is reported honestly, not hidden, because the point of this tool is to show real self-graded skill, not a marketing number. - ``model_weight_star`` (w*): the blend weight (0.0-1.0) our model earned in a model+market blend that minimizes log-loss. 0.0 means "defer entirely to the market's number"; 1.0 means "our number alone is already optimal." This is fit empirically per league/window, not asserted. - ``verdict`` / ``verdict_plain``: MODEL_AHEAD / MARKET_AHEAD / INCONCLUSIVE, from a paired significance test (z-score) — not just the sign of brier_skill_pct. - ``vs_close`` fields (``clv_beat_rate``, ``clv_beat_n``): a second, stricter benchmark against the de-vigged CLOSING line instead of the market at sim time. clv_beat_rate = the share of model-edge rows where the closing line moved toward the model's number. Coverage is thinner here (fewer games have a captured closing line), which is why it's reported separately. - ``n`` / ``reliable``: sample size behind each cell. Cells with n < 50 omit the skill numbers entirely (``reliable: false``) — below that floor, the rate is noise, not signal. Windows: ``30d`` (most current, smallest sample) and ``90d`` (steadier, larger sample). Use 90d as the primary read; use 30d to see if something is actively shifting. Freshness: the underlying file rebuilds daily (~12:50 UTC). If it is stale (>36h old), this tool returns ``{"status": "updating", ...}`` instead of presenting old numbers as current — never treat a missing ``windows`` key as "no skill data," check ``status`` first. Args: league: Optional league filter (e.g. "MLB", "NHL"). Omit for all leagues covered by the scoreboard (NBA, NHL, MLB, SOCCER, WNBA, TENNIS, LOL, CS2, GOLF, WC — CFB/NFL/CBB not yet in-season/covered). Returns: ``{status, generated_at, benchmark, close_benchmark, sample_floor_n, windows: {"30d": {...}, "90d": {...}}}`` where each window has ``overall`` (blended-across-leagues cell) and ``by_league`` (list of per-league cells, each carrying its own ``league`` code).

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• mlp_estate_planningA

  MLP Tax Computation EngineA

  Computes §1014 stepped-up basis and estate-planning analysis for one or more direct MLP positions held until death, per IRC §1014(a) (basis at death), §1014(b)(6) (community-property double step-up), §751(a) (ordinary recapture eliminated at death), §731 (distributions), and §705 (basis). Returns total deferred federal tax eliminated, §751 ordinary recapture eliminated, per-beneficiary inheritance split, community-property double-step-up amount when applicable, and the dollar advantage of holding to death versus selling today. Use when: User has one or more direct MLP positions (EPD, ET, MPLX, WES, PAA, NRP, USAC, SUN) and wants to quantify the §1014 step-up benefit for estate planning, compare holding to death versus selling now across a portfolio, model community-property double step-up for spouses in CA/TX/WA/etc., or compute per-beneficiary inheritance values across multiple heirs. Don't use for: Trust-based estate strategies (revocable trusts preserve §1014; irrevocable trusts and IDGTs typically destroy it — this tool models direct holdings only). Single-position long-horizon tax projection — use mlp_projection. Single-position sell-now-versus-hold-to-death break-even — use mlp_sell_vs_hold. 1099-DIV ETFs (AMLP, MLPX, AMZA — RIC structure receives §1014 step-up but has no §751 to eliminate because no K-1; the analysis is materially different). Limitations: Direct unit holdings only — does not model trust, IDGT, FLP, or charitable structures (these can destroy the §1014 benefit; for guidance on trust selection, see lucasandersen.ai). Federal-level only — does not include state estate tax. §751 recapture eliminated at death is estimated; exact figure depends on the partnership's actual hot-asset disposition schedule. A year-by-year basis workpaper for this position is available at https://lucasandersen.ai/tax/k1-basis-tracker?src=mcp&tool=mlp_estate_planning&surface=description. Maintained by Lucas Andersen, MS Finance, with direct positions in major midstream MLPs. Methodology auditable at lucasandersen.ai/methodology.

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• get_statsA

  Lumify Sports IntelligenceA

  Get raw, deterministic team/match statistics for a soccer, MLB, tennis, NFL, NCAAF, NBA, NCAAB, or NHL event — no odds (use get_odds) and no scoring (use get_intelligence). Payload is sport-specific: teams.home/away except tennis (players.player_1/player_2). Tennis doubles/qualifying return available:false. Form/record/H2H exclude walkovers and NFL/NCAAF/NBA/NCAAB preseason. Other sports return HTTP 400. Field catalog: https://lumify.ai/docs/reference#event-stats

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• games_on_dateA

  stat-api — Sports DataA

  Games scheduled/played on one calendar date (US-Eastern) for nba, mlb, or nhl; defaults to today. Returns the same envelope as query_table on the games table. NFL is week-based, not date-based — for nfl, use query_table on nfl/games with season_id + week filters instead. Requires an API key; rows count against quota.

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• discover_toolsA

  Mlb StatsA

  Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

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