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datalattice

mcp-chainladder

by datalattice

to_cumulative

Converts an incremental triangle into a cumulative triangle by computing running sums per row. Use to prepare data for chain-ladder reserving calculations.

Instructions

Convert an incremental triangle to cumulative (running sum per row). Unobserved cells propagate as null. Inverse of to_incremental on observed cells.

Args: incremental: Incremental triangle.

Returns dict with triangle (cumulative, same shape) and the standard disclaimer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
incrementalYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that unobserved cells propagate as null and the relationship to to_incremental, but does not cover edge cases, permissions, or side effects. The description is adequate but minimal.

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 short, front-loaded with purpose, and uses a clear Args/Returns structure. Every sentence serves a purpose with no redundancy.

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

Completeness4/5

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

Given the simple tool (1 parameter, transformation, output schema present), the description covers input, behavior, null propagation, inverse relationship, and output format. The 'standard disclaimer' reference is vague but acceptable as a convention. Nearly complete.

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

Parameters3/5

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

The only parameter 'incremental' is described as 'Incremental triangle' with 0% schema description coverage. While this adds semantic context beyond the raw schema (which only gives type), it lacks detail on shape, constraints, or examples. The description adds some value but is basic.

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 it converts an incremental triangle to cumulative via running sum per row. It differentiates itself from the sibling to_incremental by explicitly naming it as the inverse operation.

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?

The description explicitly states it is the inverse of to_incremental, providing clear guidance on when to use this tool versus its sibling. This is strong usage context.

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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