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datalattice

mcp-chainladder

by datalattice

mack_stochastic

Estimates distribution-free standard errors and coefficients of variation for chain-ladder reserve projections, providing uncertainty measures per accident year and in total.

Instructions

Mack (1993) stochastic chain-ladder error estimation. Returns distribution-free standard errors and coefficients of variation per accident row plus the totals — the canonical sensitivity check for a deterministic chain-ladder result.

Use this when the user asks about uncertainty, reserve risk, or confidence intervals. Pair it with compute_chain_ladder (use the same selected_factors for consistency).

Args: triangle: As in compute_chain_ladder. selected_factors: The factors used for the point-estimate projection. Length = n_dev - 1. excluded: Outlier exclusions to honour, same shape as in compute_chain_ladder.

Returns: - sigma2: list[float] — σ̂_j² per development period; backfilled via Mack's tail rule when only one observation is available - se_per_row: list[float] — standard error of each ultimate - cv_per_row: list[float] — coefficient of variation (SE / Ultimate) per accident row - se_total: float — SE of the sum of all ultimates (includes cross-row covariance per Mack eq. 5.15) - cv_total: float — CV of the total ultimate

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
triangleYes
selected_factorsYes
excludedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the output in detail, mentions backfilling via Mack's tail rule and cross-row covariance, but does not explicitly state side effects or authorization needs. Given the computational nature, transparency is strong though not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a clear purpose paragraph, usage paragraph, and structured Args/Returns sections. Every sentence adds value, concise yet comprehensive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all 3 parameters, explains all 5 return fields with types and interpretations, and references sibling tool for consistency. Given the output schema exists, the description is complete for an experienced user.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds comprehensive parameter details: triangle references compute_chain_ladder, selected_factors includes length constraint, and excluded explains shape and nullability. This fully compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs stochastic chain-ladder error estimation, returning standard errors and coefficients of variation. It distinguishes from sibling tools like compute_chain_ladder (point estimate) and mack_diagnostics by focusing on uncertainty quantification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises use when the user asks about 'uncertainty, reserve risk, or confidence intervals' and recommends pairing with compute_chain_ladder using the same selected_factors for consistency. Provides clear context for when to invoke.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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