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datalattice

mcp-chainladder

by datalattice

bornhuetter_ferguson

Blend chain-ladder development patterns with external a-priori ultimates to compute reserves. Results include side-by-side comparison with chain ladder to highlight divergences.

Instructions

Bornhuetter-Ferguson (1972) reserving method. Pro tier.

Combines the chain-ladder development pattern with an externally- provided a-priori ultimate per accident year, giving a result that is far less sensitive to noisy late development than pure chain ladder. The canonical "second method" for benchmarking reserves — when chain-ladder and BF agree, you can publish with confidence; when they diverge, the divergence is the finding.

Args: triangle: Cumulative loss triangle, same shape as compute_chain_ladder. a_priori_ultimates: Expected ultimate per accident-year row, usually derived from premium × expected loss ratio or a plan figure. Must have length n_acc. selected_factors: Override factor set; defaults to volume- weighted (same default as chain ladder). tail: Multiplicative tail factor. Default 1.0. excluded: Outlier exclusions, same shape as in compute_chain_ladder.

Returns either: - On success: {a_priori_ultimates, used_up_proportion, bf_ultimates, bf_ibnr, cl_ultimates, cl_ibnr, total_bf_ultimate, total_bf_ibnr, total_cl_ultimate, total_cl_ibnr} — note the chain-ladder values are returned alongside so the user can see the two methods side-by-side. - On license failure: {error, status} with status giving the upgrade URL and reason. Free-tier callers get this and can still see the API shape; no compute happens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
triangleYes
a_priori_ultimatesYes
selected_factorsNo
tailNo
excludedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: it performs the BF method, returns both BF and chain-ladder results, and handles license failures by returning an error with upgrade URL. No contradictory or missing details.

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

Conciseness4/5

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

The description is well-structured with a summary, args, and returns sections. It is slightly verbose but every sentence adds value; could be tightened slightly.

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?

Given the complexity (5 parameters, output schema, license tier), the description is thorough: it explains input formats, defaults, return fields, and error behavior. No gaps.

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%, so the description carries full burden. It provides detailed explanations for all 5 parameters, including defaults (tail=1.0, selected_factors null) and relationships, adding significant value beyond the schema.

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 identifies it as the Bornhuetter-Ferguson reserving method, explains its purpose (combines chain-ladder development with a priori ultimate), and distinguishes it from siblings like compute_chain_ladder by positioning it as a benchmarking method.

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

Usage Guidelines4/5

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

The description explains when to use it (canonical second method for benchmarking reserves) and how it relates to chain ladder, but does not explicitly state when not to use it or mention alternative tools by name.

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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