Ecommerce Finance Platform and Operations Ledger | NeonPanel

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• Forecasting Get Sales Forecast Details

  Get forecast plan details per item (SKU-level) with rich inventory attributes. Read-only; no side effects. By default returns the LATEST forecast run. To view a specific forecast, provide tool_specific.scenario_uuid and/or tool_specific.calc_period (get these from forecasting_list_sales_forecasts). Use cases: - Show forecast plan per SKU/inventory item (latest or a specific run) - Filter by brand/product_family/ASIN/SKU and sort by sales or forecast KPIs - Optionally aggregate results by compa

• Forecasting Compare Sales Forecast Scenarios

  Deep-dive forecasting comparison for multiple items. Supports selectors by inventory_ids, sku+country_code, parent_asin, or product_family (pick one) for detail mode. Aggregated mode can also run company-wide with only company_id + group_by. Compares forecast outputs across scenarios and/or forecast runs, with actuals overlay (include_actuals=true by default). Read-only; no side effects. Latest forecast evaluation: - The 'actual' scenario/dataset is always excluded when selecting forecast runs

• Forecasting Write Sales Forecast

  Validate and write user/AI-adjusted sales forecast values directly to ClickHouse for downstream planning. Consequential (writes data). Use cases: - Persist manual forecast overrides for specific items and periods - Persist AI-recommended adjustments with an explainable reason - Store sales history (actuals) for actual-vs-forecast comparison Safety: - Always include company_id and an explicit item selector (inventory_id or sku+marketplace) - Always include a reason and an author/source - Prefer

• Forecasting Generate Sales Forecast

  Generate sales forecasts using multiple statistical methods on user-provided historical data. This tool applies 6+ forecasting algorithms to your time series data and returns predictions for future periods. FORECASTING METHODS AVAILABLE: 1. rwlt_monthly_plan (DEFAULT - Recommended) - Recency-Weighted Linear Trend (monthly) - Best for: Items with recent trends, 6+ months history - Uses exponential time decay (recent data weighted more) - Softens negative trends by 50% to prevent ove

• Forecasting List Sales Forecasts

  List all available forecast runs for a company. Returns one row per distinct forecast run (unique calc_period + updated_at + dataset combination) with summary statistics. Use this tool FIRST to discover what forecasts exist before comparing or reviewing them. Each row represents a forecast run and includes: - calc_period: the calculation period (YYYY-MM) that groups the run - updated_at: timestamp when the run was written - dataset: scenario/method name (e.g. …

• Forecasting Run Sales Forecast Job

  Trigger the sales-forecast Glue ETL job that re-computes forecasts and writes the results to the Iceberg table. The job is idempotent: it deletes existing rows for the target calc_period + scenario_uuid + company_id before appending fresh results. The tool returns immediately with a JobRunId. Use forecasting_check_sales_forecast_job_status to monitor progress. Jobs typically take 3-5 minutes to complete. SCENARIO SELECTION (precedence): 1. scenario_uuids if provided (explicit override). 2. s

• Forecasting Check Sales Forecast Job Status

  Check the status of a previously started sales-forecast Glue job run. Use this after calling forecasting_run_sales_forecast_job to monitor progress. Use forecasting_list_sales_forecasts or forecasting_get_sales_forecast_details to review results after completion. IMPORTANT: Jobs typically take 3-5 minutes. Do NOT check status earlier than 3 minutes after starting the job. Possible states: - STARTING / RUNNING / WAITING: Job is still in progress. Check again in 1-2 minutes. - SUCCEEDED: Job co

• Account Lookup Asin Catalog

  ASIN catalog lookup & parent-child mapping. Resolves parent ↔ child ASIN relationships, SKU ↔ ASIN mapping, and returns product attributes from the latest inventory planning snapshot. Use cases: - Look up a child ASIN → find its parent ASIN and all siblings - Look up a parent ASIN → list all child variations (colors, sizes) - Map SKU → ASIN or ASIN → SKU - Identify the 'Hero' variant (highest revenue_share among siblings) - Get product attributes: brand, product_family, revenue class, Pareto AB

• Supply Chain List Fba Replenishment Candidates

  List items needing FBA replenishment based on projected stockout risk and inbound coverage. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. Required: company_id (integer). Calls without company_id are rejected by schema. CRITICAL: FBA inventory columns: - inbound: Amazon-reported units in transit TO FBA. - fc_transfer: Amazon-reported units transferring BETWEEN FBA centers. - available: Amazon-reported sellable FBA units. - fba

• Supply Chain List Po Placement Candidates

  List items needing PO placement based on lead time + safety stock + PO cadence coverage. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. Required: company_id (integer). Calls without company_id are rejected by schema. VELOCITY MODES (sales_velocity, default: 'planned'): • 'planned': Averages forecast units over the coverage window (lead_time + safety_stock months) starting at the arrival month — forward-looking, accounts for

• Supply Chain List Stock Replenishment Risk Items

  Identify inventory items at risk of stockout or insufficient days-of-supply. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. Required: company_id (integer). Calls without company_id are rejected by schema. Use cases: - Surface items approaching or at stockout risk - Identify products with insufficient days-of-supply buffer - Understand inbound supply timing and p80 arrival dates - Prioritize replenishment orders or expedited ac

• Supply Chain Analyze Sales Velocity

  Analyze sales velocity for selected items. Computes realized velocity signals, derives plan-horizon velocity for PO decisions, detects inconsistencies, and returns explainable recommendations. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. Required: sku (array, 1-10 values). Company selection: prefer company_id; company (numeric string) and company_ids (single value) are accepted aliases.

• Supply Chain Shipment Arrival Oracle

  Monitor in-transit shipments with predictive ETAs based on historical warehouse-to-warehouse performance. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. This tool provides real-time visibility into shipment status with: - Statistical ETA predictions: P50 (median), P80 (conservative), P95 (worst-case) arrival dates - Delay detection: Compare carrier-provided ETAs against historical route performance - Ghost shipment identificati

• Supply Chain Inspect Inventory Sku Snapshot

  Inventory SKU deep dive (raw snapshot inspection). Returns raw inventory_planning_snapshot row(s) from the latest snapshot partition for the authorized company_id(s). IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. How to use: - Best: provide sku + marketplace (US/UK) to inspect a specific item. - If you omit sku and/or marketplace, the tool will return a small sample of rows (useful to confirm snapshot data exists and to see ex

• Supply Chain List Product Logistics Parameters

  List product logistics parameters (vendor, product spec/name for POs, dimensions, box params, MOQ) from the latest inventory planning snapshot. IMPORTANT — flat input: pass all parameters at top level. Do NOT use nested query/filters/tool_specific wrappers. Inputs (choose one): - Provide inventory_ids (best when you already know the inventory IDs), OR - Provide sku + company_id (best when you only know the SKU). Notes: - Data is read from the latest available inventory_planning_snapshot parti

• Supply Chain List Bom Planning Items

  List BOM-aware inventory planning items. Use planning_source/is_assembly for assemble-versus-purchase decisions, effective_available_units and days_of_cover_incl_components for component-backed coverage, binding_component_sku for the build constraint, cumulative_lead_time_days for total lead time, and assembly_shortfall_units_actual for the finished-good shortage. product_type is display-only and must not be used as the assembly flag. Warehouse formulas already allocate shared component stock an

• Supply Chain List Component Buy Plan

  List the BOM component buy plan at one row per company and component product. It combines independent sales with dependent assembly demand and nets the requirement against the shared physical stock pool. Use net_requirement_units_actual or net_requirement_units_plan as the quantity to buy. The 12 month forecast arrays are total_plan_units_arr = independent_plan_units_arr + dependent_plan_units_arr; net_requirement_units_plan is derived from total_plan_units_arr, so never plan a component from de

• Supply Chain Write Inventory Planning Parameters

  Write item-level inventory planning parameters to NeonPanel (PUT /api/v1/companies/{uuid}/inventory-items/{inventory_id}). Consequential write tool. Use this when the user wants to save planning parameters for specific inventory items. Each write updates one inventory item's restock_data. Pass only fields that should change; omitted restock_data fields are left unchanged by the API. Important hierarchy: - Item-level values saved here override company-level defaults for that inventory item. - F

• Supply Chain Write Company Planning Defaults

  Write company-level inventory planning defaults to NeonPanel (PUT /api/v1/companies/{uuid}/settings/forecasts). Consequential write tool. Use this when the user wants to save default planning parameters for a whole company. These defaults are fallback values used when an inventory item does not have item-level restock_data overrides. Important hierarchy: - Company-level defaults are broad fallback settings. - Item-level parameters written by supply_chain_write_inventory_planning_parameters ove

• Brand Analytics Get Competitive Landscape

  Identifies the Top 3 clicked products for search terms and their relative click/conversion shares from Amazon Brand Analytics Top Search Terms report. Use this tool to: - Find 'Weak Leader' keywords where top-ranking products have low conversion shares (opportunity to displace) - Identify keywords where competitors dominate (defensive priority) - Discover share-of-voice gaps between your products and competitors - Analyze competitive positioning trends over time IMPORTANT — Data enrichment opp

• Brand Analytics Get Keyword Funnel Metrics

  Returns search funnel data (Impressions, Clicks, Cart Adds, Purchases) for keywords from Amazon Brand Analytics Search Query Performance (SQP) report. Compares 'Brand Share' against 'Total Market' to determine if the product is a conversion leader or needs SEO/PPC adjustments. Use this tool to: - Identify organic growth potential vs. conversion efficiency - Find keywords where you have high impressions but low click share (visibility gap) - Discover keywords where you have high clicks but low

• Brand Analytics Get Search Term Momentum

  Returns weekly search term momentum data from Brand Analytics search query performance: your click share, WoW delta, 4-week and 12-week rolling averages, top-3 competitor ASINs with click/conversion shares, and a computed momentum signal. Use this tool to: - Track how your click share on a search term is trending (accelerating, growing, stable, declining, collapsing) - Identify search terms where you are losing or gaining momentum vs competitors - See which competitor ASINs hold the top 3 posit

• Brand Analytics Analyze Search Query Performance

  Analyzes Search Query Performance (SQP) using a precomputed snapshot. Returns KPI metrics, deltas, efficiency ratios (click-through and conversion), diagnostic scenario classification (A: visibility, B: creative, C: conversion, D: protect), term type (branded/generic/long_tail), priority tier (1-4), and RYG signals. Supports filtering by all computed dimensions.

• Brand Analytics Analyze Search Catalog Performance

  Analyzes Search Catalog Performance using the Brand Analytics catalog performance report. Returns KPI metrics, trend deltas, and 4 high-level RYG signals (strength, weakness, opportunity, threshold) with explanations. Supports filtering by classification and signal colors.

• Brand Analytics Get Cross Sell Opportunities

  Retrieves products frequently purchased together from Amazon Brand Analytics Market Basket Analysis report. Use this tool to: - Discover complementary products for Virtual Bundle creation - Identify A+ Content cross-sell table candidates - Find Product Targeting ad campaign opportunities (target ASINs frequently co-purchased with yours) - Detect competitor products commonly bought alongside yours (defensive/offensive targeting) - Validate bundle ideas with real co-purchase data Data source: We