"Programs for Performing Statistical Analysis: R, STATA, SPSS" matching MCP tools:

• search_open_grants

  foundation-discovery

  Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)

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• patent_landscape_async

  gapup-mcp

  Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.

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• decompose_drivers

  Autario Data Analytics Platform

  CONFOUNDER-AWARE DRIVER ANALYSIS: fits ONE multiple regression of the target on ALL candidates jointly, so each effect is estimated holding the other candidates constant. Distinguishes "it was the weather" from "a promo ran at the same time": candidates too entangled to separate (VIF > 5 or pairwise |r| > 0.8) are flagged not_separable (named pairs) instead of ranked with a confident wrong number. Returns per candidate: standardized coefficient (effect size), raw slope, p-value, VIF, pairwise r (for the pairwise-vs-joint contrast), and the best lead/lag vs the target. Use this instead of find_drivers when candidates may overlap (promo calendars, weather, seasonality) or when you need honest independent effect sizes. Omit entity for entity-less private KPI series. 2-15 candidates.

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• amend_trade

  mcp

  Amend an OPEN journal trade by trade_id: move your stop or target, fix size_usd / leverage / thesis. A stop MOVE changes only the current stop (what the watchdog and close-time touch scan use); realized R stays measured against your INITIAL stop, so trailing to breakeven can't inflate R. To fix a genuine fat-finger in the original entry or stop, also pass correct_entry=true — that resets the R basis (disclosed in the response). asset/side can't be amended — void and re-log for that. Pro.

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• get_track_record

  mcp

  Get the realised track record: win-rate + R-multiple distribution. Aggregates actual closed outcomes (tp_hit, trail_stop_hit, stop_hit, position_closed) into an honest scorecard — win_rate, wins/losses, and the realised-R distribution (mean/median/min/max/total). Pass a ticker for its per-ticker record; omit it (or pass "") for the portfolio-wide record. HONESTY: small samples are flagged `illustrative: true` with an explanatory `note`, and `win_rate` is null when no closed P&L exists. Never present an illustrative record as a reliable hit-rate. Every number is grounded in stored outcome events — none are fabricated. This is the ground-truth check on signal quality: a high stated R:R only holds if the realised-R mean is positive and the stop-hit rate is low. Args: ticker: Stock ticker (e.g. "AAPL"), or "" for portfolio-wide. Returns a dict with scope, sample_size, win_rate, realised_r, and note.

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• get_biography

  Jennifer Rebholz, Attorney - Knowledge Base

  Use when someone asks for a biography of Jennifer Rebholz formatted for press, media, CLE programs, speaker introductions, or event listings. Returns short, medium, and full biography versions ready for direct use.

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• find_drivers

  Autario Data Analytics Platform

  KILLER ANALYSIS: given a target KPI + multiple candidate indicators, rank which candidates best predict the target by correlation strength. Perfect for "what moves my KPI?" questions. Returns ranked list with r, p-value, R² for each candidate. Maximum 30 candidates per call.

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• patent_landscape_async

  mcp-knowledge

  Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.

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• list_services

  Escola de Rádio TV & Web (ER+)

  Lista serviços oferecidos pela ER+ ALÉM dos cursos regulares: 'demo' (gravação de demo profissional com Ruy, R$ 350), 'mentoria' (mentoria 1:1 com Ruy, R$ 250/h), 'b2b' (treinamentos corporativos pra empresas). Use quando aluno perguntar sobre demo, mentoria, treinamento empresarial. Pra cursos regulares, use list_courses.

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• tf_premium_anomalies

  terminalfeed

  Premium anomaly screen. Surfaces statistical outliers across feeds in one ranked list: z-score outliers (|z|>2) over a trailing 30 daily-observation window for VIX (FRED VIXCLS) and the 10y treasury yield (FRED DGS10), plus threshold flags for extreme crypto Fear & Greed (<=20 or >=80), large 24h crypto market-cap moves (>5%), and elevated M4.5+ earthquake counts (>=8 in 24h, USGS). Each anomaly carries type, signal, value, baseline, z_score where applicable, severity, and a description. Distinct from world-deltas (a raw time-sorted event log with no statistical filtering): this answers "is anything statistically unusual right now". A screen, not a prediction. Costs 2 credits ($0.04 USDC). Requires Authorization: Bearer tf_live_<64-char-hex>.

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• subjects

  Makstat Mk

  Browse the North Macedonia State Statistical Office (MakStat) PxWeb subject tree under the /MakStat database. Empty path returns top-level folders (type 'l'); drill with sub-paths to reach tables (type 't', '.px' suffix). Returns folder/table list for the given path.

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• openaq_get_measurements

  openaq-mcp-server

  Historical measurement series for one pollutant at one station over a date range — for trend analysis and "was last week worse than the monthly average?". Pass a locationId and a parametersId and work in stations — you get the series for that pollutant at that station. Choose aggregation: raw (every reported value), hourly, or daily — daily and hourly add a per-bucket statistical summary (min, median, max, mean, sd). Large ranges produce thousands of rows and spill to a DataCanvas: the response returns a preview plus a canvasId and table name you query with openaq_dataframe_query. Values carry their unit; the server never converts between µg/m³, ppm, and ppb.

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• search_tiktok

  Hermoso

  Organic TikTok keyword search (there is NO TikTok ad library) — top-performing videos to mine for hooks/trends/remixable creative. Returns compact JSON {desc, author, handle, plays, likes, link, cover} per video, ranked by plays. Use research_ads for open-ended research. Spends ScrapeCreators credits (~1).

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• search

  aeX402 RPC (Solana + Robinhood Chain)

  Search the aeX402 universe by keyword: tokens (Robinhood Chain + HyperEVM census), known programs/contracts (Solana, Robinhood, HyperEVM, Slonana), and live AeX402 AMM pools. Returns a result list; pass a result id to the fetch tool for full detail. Use the specialist tools (search_tokens, discover_programs, aex402_listPools) when you already know the entity type.

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• adw.adw_618

  AlpineDataWorks Intelligence Server

  Returns a 0-100 frontier-hardware research-momentum score (Semantic Scholar publication counts for quantum computing, solid-state batteries, and neuromorphic computing — 90-day windows vs. baseline, summed) with trend, z_score, per_variant_w0, and window_counts. Call when the user asks about emerging-tech R&D acceleration, quantum/battery/neuromorphic research trends, or pre-patent signals, or when timing deep-tech investment, corporate R&D strategy, or technology-scouting decisions. Updates: daily.

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• spice_simulate

  calc.engineer

  Run a SPICE circuit simulation directly in the worker. Accepts a standard SPICE netlist and performs operating-point (.op), DC sweep (.dc), AC frequency sweep (.ac), or transient (.tran) analysis using a built-in modified-nodal-analysis engine with Newton-Raphson nonlinear solving. Supports R, L, C, V, I sources (DC/AC/SIN/PULSE), diodes, BJTs (Ebers-Moll), MOSFETs (Shichman-Hodges level 1), controlled sources (E/G), .param expressions, .subckt/X hierarchical subcircuits, .measure and .four (Fourier/THD), and a built-in parts library (reference 2n3904/2n2222/1n4148/1n5819/2n7000/tl072/lm358/led_red etc. by name and the model is auto-injected), up to 150 components. Returns voltage vectors for all circuit nodes (magnitude + phase for AC). Use spice_template to generate netlists from parameterized templates.

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• timer_555

  calc.engineer

  Calculates timing parameters for the ubiquitous NE555 / LM555 timer IC in astable (free-running oscillator) and monostable (one-shot pulse) modes. In astable mode, computes frequency, period, duty cycle, and HIGH/LOW durations from R1, R2, and C using f = 1.44 / ((R1 + 2·R2) · C). In monostable mode, computes pulse width from R and C using t = 1.1 · R · C. Standard 555 astable duty cycle is always >50%; for 50% duty cycle use a diode across R2. Useful for generating clock signals, PWM, delays, and debounce circuits. Chain from ohms_law for power calculations or resistor_color_code for component selection.

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• get_feature_stats

  aTars MCP

  USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• calc_interest

  Math MCP Learning

  Calculate compound interest for investments. Formula: A = P(1 + r/n)^(nt) Where: - P = principal amount - r = annual interest rate (as decimal) - n = number of times interest compounds per year - t = time in years Examples: compound_interest(10000, 0.05, 5) # $10,000 at 5% for 5 years → $12,762.82 compound_interest(5000, 0.03, 10, 12) # $5,000 at 3% compounded monthly → $6,744.25

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• get_capital_allocation_profile

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Get a multi-year capital allocation breakdown for a US public company. Shows how management deploys cash across all six categories — capex, R&D, M&A, dividends, buybacks, and debt — plus pre-computed deployment ratios (% of operating cash flow) and over-distribution flags. Use this tool when the user asks: how does a company allocate capital, what's the buyback-vs-dividend mix, is the company over-distributing, is growth funded by R&D or M&A, what's the cash-return-ratio trend, or any 'where does the money go' question — including owner-earnings (Buffett-style) and reinvestment-rate (Damodaran-style) analysis. Data sourced from annual 10-K filings; PIT-safe via as_of_date. R&D is included as a deployment category (the primary growth-reinvestment vehicle for knowledge-economy firms), but since it's already deducted before operating cash flow, `rd_pct_ocf` is INFORMATIONAL and `total_deployment_pct_ocf` EXCLUDES R&D to preserve the cash-flow identity (OCF = capex + M&A + dividends + buybacks + debt repayment + Δcash). The `flags` object carries pre-computed booleans: `buybacks_exceed_fcf`, `total_returns_exceed_fcf` (buybacks + dividends > FCF), and `debt_funded_distribution` (over-distribution funded by leverage vs cash). Available on all plans.

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