forecast-mcp
Enables demand forecasting and replenishment recommendations using statistical models (Syntetos-Boylan classification, AutoETS, TSB) and provides tools for forecasting, evaluation, and order quantity calculation.
Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.
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RohanSingh02
Source: Glama
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Install from
The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.
These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.
list_skusList every SKU/series id available in the loaded dataset.
classify_demand_patternClassify a SKU's demand pattern (Syntetos-Boylan: smooth / erratic / intermittent / lumpy / cold_start) and the model tier it routes to.
forecast_seriesForecast future demand for a SKU. Automatically classifies the series first and routes to the matching model: mean fallback for cold-start SKUs, TSB for intermittent demand, AutoETS for regular continuous demand.
evaluate_forecastBacktest a SKU's forecast: hold out the last `test_size` days, forecast them from the remaining history, and score with MASE (scale-free, so it's comparable across SKUs with very different demand volumes).
recommend_replenishmentRecommend a reorder quantity for a SKU: forecasts demand, then applies a reorder-point / safety-stock formula against current on-hand stock.
explain_forecastReturn a plain-language explanation of why a SKU was routed to the model tier it was, for surfacing to a non-technical user.
FORECAST_MCP_DATAexport =/path/to/demand.csvFORECAST_MCP_TRANSPORTFORECAST_MCP_HOSTFORECAST_MCP_PORTTool annotations
No tools have read-only/destructive annotations
Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.
All four hints declared on every tool
6/6 tools missing one or more hints — list_skus (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); classify_demand_pattern (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); forecast_series (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +3 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.
For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.
License file
No license file
Add a LICENSE file (MIT, Apache-2.0, etc.).
Tool test coverage
4/6 tools referenced in tests (67%)
Write tests that reference each tool by name so every tool has at least one test.
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