This server exposes two price elasticity tools with clear mathematical implementations but significant documentation and schema gaps. Tool names are verb-based (analyze, simulate_price_change) and reasonably clear. However, parameter descriptions are minimal, output schemas are not formally documented, and error handling is basic. The code shows good implementation quality (log-linear regression, revenue optimization) but lacks the documentation discipline required for LLM integration. No schema validation, enum constraints, or structured error guidance.
Tools (2)
analyzeread onlysource verified60/100
Full price elasticity analysis. Uses log-linear regression to estimate the price-demand relationship, then simulates revenue across a price range to find optimal points.
Output schemas not documented. The analyze() tool returns a complex dict with elasticity, optimal_price, revenue_curve (list of dicts), and recommendations, but the LLM has no schema definition to understand this structure.
Parameter descriptions are too brief and lack actionable detail. 'List of {price, units_sold} dicts' tells the LLM the container type but not the valid range for price/units (must be > 0?), format (integer or float?), or how many data points are minimally sufficient (code requires ≥2, should be in description).
No enum or range constraints for parameters. 'steps' defaults to 50 with no max bound documented, LLM could pass 10000, causing performance issues. 'price_range' expects a dict with 'min' and 'max' but this is untyped and not formally validated in the schema definition visible to the agent. Per pattern:constrained-input, known sets should be enums; ranges should have explicit bounds.
Expand parameter descriptions to 60-100 characters each with actionable constraints. E.g., 'List of price-demand data points. Each dict must have price (float > 0) and units_sold (float > 0). Minimum 2 valid data points required for regression; more data improves accuracy.' Similarly, 'Number of price points to simulate in the revenue curve. Must be 10 - 500; higher values improve optimization precision but increase computation time. Default: 50.'
Add enum or range constraints to all parameters. Define steps as integer with minimum 10, maximum 500. For price_range, specify that price_range must be a dict with keys 'min' (required, float > cost_per_unit) and 'max' (required, float ≤ current_price * 3.0 or similar upper bound). Document: 'If price_range is omitted, defaults are min=cost_per_unit*1.1 or current_price*0.5, max=current_price*2.0.'
Enhance error messages with recovery guidance. When ValueError is raised, return a structured error dict with: { error_code: 'INSUFFICIENT_DATA_POINTS', message: 'Need at least 2 valid data points. Received 1 after filtering.', suggestions: ['Ensure all price values are > 0 and all units_sold values are > 0.', 'Add more historical data points covering a wider price range.'] }. Do this for division-by-zero (zero variance) and other failure modes.
Error handling provides no recovery guidance. Code raises ValueError with minimal context ('Need at least 2 data points', 'Insufficient valid data points') but does not tell the LLM what to do next. Per pattern:recovery-guide, errors should suggest corrective actions like 'Ensure each data point has price > 0 and units_sold > 0, then retry with at least 2 points.'
Tool descriptions lack clear 'when to use' guidance. 'Full price elasticity analysis' and 'Quick simulation' are vague about when to call each. The LLM cannot determine: should I call analyze() first, then simulate_price_change() with the elasticity result? Or are they alternatives? When to use it instead of similar tools?
No validation of input data quality. The code silently filters out price/units ≤ 0 but does not warn the LLM how many data points were dropped or whether the remaining set is statistically valid. If 10 of 12 data points are filtered, results may be unreliable. Per pattern:actionable-errors, multi-step operations should return per-item success/failure or at minimum warn about data loss.
analyze
Document the relationship between analyze() and simulate_price_change(). Add to analyze description: 'Use this first to compute elasticity and find the optimal price point. Once you have the elasticity coefficient, call simulate_price_change() to model the impact of alternative pricing scenarios.' Add to simulate_price_change: 'Use this after analyze() if you want to model a specific price change without re-computing elasticity. Requires elasticity value from a prior analyze() call.'
Add a data_quality_warnings field to the analyze() response. Return: { ..., data_points_discarded: integer, data_discard_pct: number, notes: array<string> }. Include warnings like 'Discarded 2 of 10 data points with price ≤ 0 or units ≤ 0' and 'Price range narrow (min=$10, max=$50); elasticity estimate may have high uncertainty.' This lets the LLM warn the user about data quality issues.
Add per-parameter validation with clear error messages. Before computing regression, validate: cost_per_unit >= 0, current_price > 0, steps in [10, 500], price_range.min > 0, price_range.max >= price_range.min. Return errors like: 'Invalid steps: got 1000, must be 10 - 500' rather than silent failures.
Clarify the statistical assumptions in tool descriptions. Add to analyze: 'Uses log-linear regression (ln(Q) = a + b*ln(P)) assuming a linear relationship in log-log space. Works best with price data spanning at least 2x range (e.g., $10 - $50). Results are indicative; validate with A/B testing before major price changes.' This sets realistic expectations.