Skip to main content
Glama
datalattice

mcp-chainladder

by datalattice

tail_extrapolation

Fit exponential or inverse-power tail models to late development factors and extrapolate to estimate the tail factor for chain-ladder reserving.

Instructions

Fit parametric tail models to the late development factors and extrapolate forward. Pro tier.

Fits two candidate models — exponential decay (ln(f_j - 1) = a + b·j) and inverse-power (ln(f_j - 1) = a + b·ln(j+1)) — picks the better R², and returns the implied tail factor for plugging back into compute_chain_ladder(tail=…).

Use this when the triangle obviously hasn't reached ultimate by the last observed development period — i.e., the last selected factor is still meaningfully above 1.

Args: selected_factors: The factor set you want to extrapolate from. n_extra: How many extra development periods to project. Default 6 (covers most P&C lines).

Returns either: - On success: {fits[], recommended, summary} — each fits entry has model, parameters, r_squared, extrapolated[], tail_factor. - On license failure: {error, status}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
selected_factorsYes
n_extraNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It explains the fitting process (two models, R² selection), return types (success or license error), and mentions the 'Pro tier' license. It is transparent about the computation but could add more detail on side effects (none) or prerequisites beyond license.

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?

The description is well-structured with a header, explanation, usage guidance, parameter descriptions, and return format. It is concise with no wasted words, front-loaded with the core action.

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?

Despite the tool's complexity (model fitting, multiple return paths, license dependency), the description covers all essential aspects: what it does, when to use, parameters, return structure, and error handling. The presence of an output schema further completeness.

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?

The input schema has 0% description coverage, but the description provides clear explanations for both parameters: selected_factors as 'the factor set to extrapolate from' and n_extra with default 6 and context 'covers most P&C lines'. This adds significant meaning beyond the schema types.

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 fits parametric tail models to late development factors and extrapolates forward, specifically for chain ladder tail factor. It distinguishes itself by mentioning plugging into compute_chain_ladder, a sibling tool, and specifying the two models used. The purpose is unambiguous and specific.

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 explicitly states when to use the tool: when the triangle has not reached ultimate and the last factor is above 1. It also links to compute_chain_ladder for integration. However, it does not explicitly mention when not to use it or list alternative tools, though no direct alternative exists among siblings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/datalattice/mcp-chainladder'

If you have feedback or need assistance with the MCP directory API, please join our Discord server