Single tool 'estimate_ate_ipw' has a detailed description (194 chars, within baseline 34-392 range) explaining the statistical method clearly. Input schema is present with three array parameters (covariate_x, treatment_t, outcome_y), each with type and description. However, critical gaps prevent higher scoring: (1) Tool name does not start with action verb, 'estimate_ate_ipw' is a noun phrase, not 'estimate_ate' or 'calculate_ate'. LLMs infer intent from verb-first names; this violates pattern:tool naming convention. (2) No output schema documented, the code returns a dict with 6 fields (ate_ipw, naive_ate, mu1, mu0, w0, w1) but the tool definition provides no schema for these outputs. LLMs need to know what fields to expect.' (3) No error handling guidance, the code has no validation of input array lengths, no checks for empty arrays, no recovery hints. If arrays are mismatched or empty, the tool will fail silently or crash. (4) Parameters lack constraints, arrays accept any numeric values with no bounds, no validation that arrays have matching lengths, no guidance on expected ranges (e.g., treatment_t should be 0 or 1 only). (5) No tool annotations (readOnlyHint, idempotentHint) despite being a stateless, read-only statistical computation.

• Rename tool from 'estimate_ate_ipw' to 'estimate_ate' (verb-first, concise). If multiple estimation methods exist, use 'estimate_ate_ipw', 'estimate_ate_aipw', etc., all verb-first.
• Add explicit output schema to tool definition: {type: 'object', properties: {ate_ipw: {type: 'number', description: 'IPW-adjusted ATE estimate'}, naive_ate: {type: 'number', description: 'Unadjusted difference in means'}, mu1: {type: 'number', description: 'Treated group mean'}, mu0: {type: 'number', description: 'Control group mean'}, w0: {type: 'number', description: 'Logistic regression intercept'}, w1: {type: 'number', description: 'Logistic regression slope'}}}
• Add input validation: require all three arrays to have matching length (n >= 3); validate treatment_t contains only 0 or 1; validate covariate_x and outcome_y are numeric. Return actionable error: 'Arrays must have equal length. Got covariate_x: 5, treatment_t: 3, outcome_y: 5.'
• Add idempotentHint: true to tool definition (no side effects, deterministic output given same input). This signals to agents that retries are safe.
• Document parameter constraints in descriptions: covariate_x: 'Array of numeric covariate values (any range, e.g., -10 to 10)'; treatment_t: 'Array of binary treatment indicators (0 or 1 only, must match length of covariate_x)'; outcome_y: 'Array of numeric outcome values (must match length of covariate_x).'