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Glama
Cubiczan

finance-engines-mcp

by Cubiczan

Product margins per tonne

product_margins

Compute per-product revenue, cost, and margin per metric tonne by applying metal assays, payable percentages, and index prices, with inventory valued at market.

Instructions

Compute per-product revenue, cost, and margin per metric tonne. Revenue/MT = sum over metals of (assay% x payable% x index price), marked to market against inventory. Returns one record per product with metal contributions and inventory valuation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configNoMargin config (products, indices, cost_per_mt, inventory_mt, sensitivity_shocks, contracts). Defaults to the bundled sample config.
pricesNoIndex prices {symbol: usd_per_tonne}. Defaults to the bundled sample feed.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description must carry the burden. It discloses the calculation method, market-to-market aspect, and output structure, but it doesn't explicitly state whether the tool is read-only or if it modifies state. It also omits dependencies like the need for sample configs or feed availability.

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?

Two concise sentences deliver purpose, formula, and output. The critical formula is front-loaded, and there is zero wasted wording.

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?

For a compute tool without an output schema, the description explains the return format (one record per product with metal contributions and inventory valuation) and gives the calculation formula. It could mention side effects or data prerequisites, but given the tool's analytical nature, it is reasonably 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?

Schema coverage is 100% with detailed descriptions for both parameters (config and prices). The description adds context about how the formula uses these inputs, but it largely restates the schema. It doesn't clarify nested structures beyond what the schema already provides.

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

Purpose4/5

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

The description clearly states it computes per-product revenue, cost, and margin per metric tonne, with a precise formula. It distinguishes itself from siblings like price_sensitivity or breakeven by focusing on margin computation, though it doesn't explicitly name alternatives.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus siblings. It doesn't mention prerequisites, exclusions, or typical scenarios, leaving the agent to infer based on the tool name alone.

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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