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Forecast product demand using moving averages, trend detection, and seasonality decomposition.
This MCP server has a single tool 'forecast' with partial schema documentation and moderate descriptions. However, critical gaps prevent higher scoring: (1) No visible MCP server registration code, only a standalone Python SDK client class is shown. No evidence of tool registration via MCP protocol, resource handlers, or prompt definitions. (2) The tool name 'forecast' is a generic verb lacking specificity, 'forecast_demand' or 'forecast_product_demand' would better match agentic tool naming patterns. (3) Input schema is partially documented but lacks several critical constraints: numeric ranges (alpha must be 0-1, safety_stock_factor should have bounds), enum definitions, and detailed validation rules. (4) No output schema documentation visible, the code returns a complex dict with nested objects (forecast array, reorder_recommendation dict, seasonality_index list) but these structures are not formally declared. (5) No error handling patterns: the code checks len(sales_history) < 3 but returns a generic ValueError with minimal context. Missing are recovery guidance, categorization (retryable vs fatal), or structured error responses. (6) Description adequacy: tool description is 146 chars (within 10-1024 range) but lacks WHEN to use it, prerequisites clarity, or comparison to alternatives. Parameter descriptions vary: 'sales_history' is good (45 chars), but 'alpha' is vague (47 chars, no mention of 0-1 constraint).
Forecast future demand using exponential smoothing, linear trend detection, and multiplicative seasonality decomposition.
Tool name 'forecast' is too generic and lacks action verb clarity. Production naming should be 'forecast_demand' or 'forecast_product_demand' to help LLMs distinguish from other forecast tools (forecast_revenue, forecast_inventory, etc.).
No visible MCP server implementation, provided code is only a standalone Python SDK client class (DemandForecastingClient). No MCP protocol handlers, tool registration, transport implementation, or server bootstrap code are shown. Cannot verify this is actually an MCP server vs a Python library.
Output schema is not formally documented. The forecast() method returns a complex nested dict (forecast list, reorder_recommendation dict, seasonality_index list, summary string) with no schema declaration visible to LLMs. LLMs cannot reliably chain this tool's output to others without knowing the return structure.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 26 | 2026-07-28+ | v2 |
Numeric parameter constraints are not formalized. 'alpha' should declare range [0, 1] in both description and schema. 'safety_stock_factor' should have reasonable bounds (e.g., [0.5, 3.0]). 'forecast_periods' and 'lead_time_periods' should declare minimum/maximum (e.g., 1-120). Absence of bounds lets LLMs pass absurd values (alpha=999, forecast_periods=-5).
Error handling is minimal and non-recoverable. The code raises ValueError('Need at least 3 historical periods.') with no guidance on what to do next. No error categorization (retryable vs user-fixable), no recovery suggestions (e.g., 'Try again with more historical data'), no structured error response format for LLM consumption.
Parameter descriptions lack specificity and validation rules. 'alpha' is described as 'Exponential smoothing factor (0-1)' but the actual constraint [0, 1] is embedded in text, not formalized. 'safety_stock_factor' description mentions '(default 1.5x avg demand)' which is misleading, the default is 1.5 (a multiplier), not a calculation. LLMs may misinterpret this.
Tool description lacks WHEN to use context. Current: 'Forecast future demand using exponential smoothing, linear trend detection, and multiplicative seasonality decomposition.' Missing: prerequisites (min 3 periods of history), use case context (supply chain planning, inventory management), comparison to alternatives, or output interpretation guidance.