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cashflow-mcp-server

Convert between rate conventions

cashflow_convert_rate
Read-onlyIdempotent

Convert annual and monthly interest rates under compound or divide conventions to avoid distorting cash flow calculations.

Instructions

Convert an annual rate to a monthly one and back, under either convention.

'compound' is the discounting convention: (1 + annual)^(1/12) - 1. 'divide' is the loan convention: annual / 12. A 12% annual rate is 0.9489% per month compounded but 1% per month divided, and using one where the other belongs quietly distorts every downstream number.

Args:

  • value (number): The rate to convert, e.g. 0.14 for 14%.

  • direction ('annual_to_monthly' | 'monthly_to_annual'): Which way to convert.

  • convention ('compound' | 'divide'): 'compound' for discounting, 'divide' for amortization.

Returns: input, output, and both conventions side by side so the difference is visible.

Examples:

  • Use when: "What monthly rate should I discount at for a 14% cost of capital?"

  • Use when: "Is 1% a month the same as 12% a year?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesThe rate to convert.
directionNoWhich way to convert.annual_to_monthly
conventionNo'compound' for discounting, 'divide' for amortization.compound

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
outputYes
dividedYes
directionYes
compoundedYes
conventionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, establishing the tool's safety profile. The description adds valuable behavioral context by explaining the formulas for each convention, warning that mixing conventions 'quietly distorts every downstream number,' and noting the return format includes both conventions side-by-side. This goes beyond the annotations and enriches the agent's understanding.

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 and appropriately sized. It starts with the core purpose, then explains the conventions, lists arguments, describes the return, and ends with usage examples. Every sentence adds value—the warning about distortion and the concrete examples are essential, not filler. It is 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?

Given the tool's simplicity and the richness of the schema (100% coverage) and output schema, the description is complete. It covers all parameters, explains the math, provides usage examples, and highlights the critical pitfall. The agent has everything needed to correctly select and invoke the tool without additional context.

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 coverage is 100% and each parameter has a description, so the baseline is 3. However, the description adds significant meaning: it provides the exact mathematical formula for the 'convention' parameter ('(1 + annual)^(1/12) - 1' vs 'annual / 12') and gives a concrete example for 'value' ('0.14 for 14%'). This goes well beyond the schema's generic wording, making the semantics much clearer.

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 converts between annual and monthly rates under two conventions, using specific verbs and resources. It distinguishes itself from sibling tools (analysis, modeling, evaluation, etc.) by focusing solely on rate conversion, so an agent can easily identify its unique purpose.

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 provides two 'Use when' examples that illustrate concrete scenarios, guiding the agent on when to invoke this tool. While it doesn't list alternatives, the clear examples and distinct focus make the intended usage unambiguous relative to the sibling tools.

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