"Business Intelligence and Research for Improving Sales Strategies" matching MCP tools:

• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• inventory_optimizer

  Inventory Intelligence & Optimizer

  Works out what a business should buy, how much, and when. Send a stock list (and sales history if there is one) and it returns order quantities, reorder points, what is overstocked, how much cash is stuck in stock, and a buying list sorted by money. ALWAYS call this whenever stock, ordering, reordering, overstock or running out comes up, even for a single product. Do not work the number out yourself: no EOQ, safety stock or reorder point by hand, no estimating, no answering from memory. Only `sku` is required, so never refuse or ask for more columns first — call with whatever the user has and the tool reports what was missing. Send their column names exactly as they are. Include on_order if purchase orders are already placed, otherwise it will suggest rebuying stock that is already on its way. Use this when the data is about 200 products or fewer and about 2,500 sales rows or fewer; for anything bigger use inventory_optimizer_get_engine instead.

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• compare_backtests

  Backtest360

  Run several strategies on the same data and compare side by side. One quota-counted call, but compute scales with the number of strategies. If the wall-clock compute budget is exceeded, the call fails with a tool error (504) instead of returning partial results — narrow the request (fewer strategies, shorter date range, coarser frequency) and retry. Args: data_source: Shared data source (same shape as run_backtest). strategies: List of {"label": str, "strategy": {...}, "execution": {...}?} entries. Labels need not be unique or id-safe — they are echoed back verbatim in the result. include_benchmark: Add a buy-and-hold benchmark to the comparison. response_detail: Shaping level applied to each strategy's result. trades_limit: Max trades per strategy when detail is 'full'. Returns: {"strategies": [{"label", "result"}, ...], "equity_curves": {...}, "alignment"?}, each result shaped at the requested detail. When a benchmark is included, non-benchmark entries also carry "relative" (beta, alpha, information ratio, etc.). A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error.

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• arena_compare_strategies

  Backtesting Arena

  Which of these strategies performed best on the same data? Run 2–5 strategies against the SAME pair, interval and date range and return per-strategy metrics plus a comparison summary (best by CAGR, best by win-rate, worst by drawdown). Use this when the user asks which of several strategies fits a market — it holds the pair, interval and requested date range fixed, which a series of separate arena_run_backtest calls does not guarantee. What it does NOT equalize is the EVALUATION window: a strategy with a long warmup starts trading later, so compare actual_date_from across the runs and check result.benchmark before ranking by CAGR. For one strategy across many pairs use arena_run_universe_backtest instead. Caveat worth passing on: comparing N strategies and reporting the winner IS multiple testing — the winner’s edge is upward-biased. arena_get_robustness_field puts a counted N on that. Sequential, expect 10–50s. Per-day quota: Pro=20, Power=200. [API Pro tier]

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• c2b_append

  Versium REACH

  Get business demographic details for consumers. Identify the consumer with a LinkedIn URL, or with first name, last name, and one of: email, phone, or city and state. Retrieve job title, seniority, department, business email, LinkedIn profile, and complete business information. Use this tool when users ask for 'C2B', 'c2b', or 'Consumer to Business Person' data **Tips for Best Results:** - Provide full name and consumer email for best match quality - LinkedIn URLs must be in format: linkedin.com/in/username - Use `rcfg_require_email` to return only records with business email - Use `rcfg_require_value` to filter by job title, department, or other attributes

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• china_ecommerce_intel

  mcp-knowledge

  Chinese e-commerce intelligence for the ZH diaspora (50M+), import-export teams, brand IP enforcement, MENA/Africa entrepreneurs sourcing from China, and brand monitoring. Covers Taobao, Tmall, JD.com, Pinduoduo, 1688.com (B2B) and AliExpress (cross-border). Five modes: • product_search — search products by keyword across CN platforms. Returns title ZH/EN, price CNY + USD estimate, sales 30d, rating, seller info, product URL. • seller_profile — full seller/supplier dossier: factory vs reseller detection, certifications (ISO, BSCI, CE), rating, years in business, main categories. • price_history — 12-month price trend for a product (live current price + seasonal model for CN shopping festivals: 11.11, 6.18, CNY). • brand_monitoring — detect counterfeits and grey market listings: price anomaly detection (>50% below MSRP = suspicious), counterfeit keyword scan, risk score 0-100. • market_intel — category overview: top 5 sellers by market share, avg/median price, volume estimate, price range. Data quality note: LIVE data from Taobao/Tmall/JD/Pinduoduo REQUIRES AICI_RESEARCH_PROXY_URL with CN residential routing (Bright Data -country-cn). Without proxy: AliExpress (cross-border) + curated category fallback available. Input formats for seller_profile: 'platform:id' e.g. 'aliexpress:123456', '1688:87654321', 'tmall:apple-store-official'. Input formats for price_history: AliExpress product URL or numeric product ID.

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• get_ml_trends

  SnowSure — Snow & Ski

  Fetch SnowSure-unique ML/AI trend datasets from the public REST API. Use for powder-day leaders, bluebird-day leaders, bluebird predictions, improving/stable/declining score pulse, per-model accuracy weights, daily SnowSure score component history, ML extended outlook (days 8–14), global forecast trust, and powder/bluebird event logs. Start with dataset=catalog. Its leaderboards read CURRENT-season counters and are global — they take no season and no country/state filter. For a past season, or for any ranking scoped to a state, province, country or region ("most snow days in Maine last season", "rank BC resorts by season snowfall"), use get_season_leaderboard instead. Prefer get_insights for narrative intelligence cards; use this for raw rankings and time series.

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• lion_trend_intent_signal_json

  LION Trend Intent MCP

  Hardware-wallet buyer-intent + self-custody onboarding-friction intelligence as JSON. Non-PII public-signal feed covering: Ledger vs Trezor comparison demand, Bitcoin-only wallet research, under-$100 wallet shopper queries, DeFi hardware-wallet intent, and adjacent decision-stage crypto purchase research. Designed for agent routing, affiliate / comparison-content workflows, and research context. Pay $0.01 USDC on Base mainnet via x402 (HTTP 402 + EIP-3009 transferWithAuthorization) at the paid route. This MCP tool does NOT return the dataset; tools/call returns payment-required metadata so an x402-capable client can settle and fetch the JSON directly.

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• get_dealer

  OpenDealer MCP Server

  Get comprehensive information about a specific dealership. Returns Google-enriched dealer knowledge optimized for assistants: • Name, address, phone, website • Google rating, review count, hours, business status • Inventory count and OpenDealer profile links • Contact points for sales / customer service Use this when a shopper asks "tell me about X dealership" or needs hours/ratings for a known dealer. Prefer a slug from dealers_near or search results. CRITICAL: Only use URL fields from the response (website, urls.*). NEVER invent or construct URLs.

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• intelligence.gender_risk

  DPX — Institutional Cross-Border Settlement

  Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.

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• business_snapshot

  Taokeh MCP server

  A one-glance health check of your business: today's and this-month's sales and expenses, so far. Sales and expenses ONLY — if you want the fuller morning read (cash position, who owes you, pending approvals, tax position, low stock) call daily_brief instead; it includes everything here.

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• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• sales-intelligence__enrich_company

  enterprise-mcp-gateway

  [Sales Intelligence] Enrich a single company with domain, description, industry, employee band, social profiles, and (where available) email patterns. Wraps `nexgendata/company-enrichment-tool`. Accepts either a free-form company name ("Stripe") or a domain ("stripe.com"). Args: domain_or_name: Company name or domain.

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• get_device_clearances

  Stratalize Healthcare

  FDA 510(k) device clearance history from OpenFDA by device name. Returns K numbers, applicants, decisions, and receipt dates. Use for medtech competitive intelligence, regulatory pathway research, and supplier qualification. Source: FDA 510(k) database. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.

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• get_strategy_leaderboard

  OneQAZ Trading Intelligence

  Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edge surface for trust auditing. Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?", "top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?". When to call: final trust-validation step. Prerequisites: none. Next steps: market://{market_id}/signals/summary for live signals. Caveats: `min_trades` filter enforces statistical validity. Strategies are paper-tested, not real-money executed. Args: market_id: Market identifier (crypto, kr_stock, us_stock) target_market: Alias for market_id (backward compat) top_n: Top N strategies to return (default 20) limit: Alias for top_n (client-compat) min_trades: Minimum trades count for inclusion (default 10) include_per_symbol: Include per-symbol PG partition results (default True) Disclaimer: Information only, not investment advice.

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• list_routing_strategies

  Courier

  List routing strategies in the workspace. Returns metadata only; use get for full details.

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• intelligence.gender_risk

  DPX — Institutional Cross-Border Settlement

  Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.

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• adw.adw_262

  AlpineDataWorks Intelligence Server

  Returns a 0-100 US durable-goods orders momentum score (FRED DGORDER, monthly; recent vs trailing-mean % deviation, scaled; history since 1996) with momentum_score, recent_value, and deviation_pct. Call when the user asks about business capex acceleration, capital spending, manufacturing demand, factory or equipment orders, or the industrial cycle, or when timing capacity expansion, industrial equipment sales, or capex-cycle positioning decisions. Updates: monthly.

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• run_safe_analytics

  GleanMark Trademark Search

  Run a constrained business-level analytics query without exposing schema details. This is the default fallback for bespoke rankings, counts, snapshots, and timelines across owners, firms, and correspondents. Prefer this before chaining search, summary, or web research tools for aggregate business questions.

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• read_strategy_list

  Arcadia Finance

  Get Arcadia LP strategies. Use featured_only=true for curated top strategies (recommended first call). Returns a paginated list with 7d avg APY for each strategy's default range. Increase limit or use offset for pagination. All APY values are decimal fractions (1.0 = 100%, 0.05 = 5%). For full detail on a specific strategy (APY per range width), use read_strategy_info.

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