Skip to main content
Glama

tvm-fund

calculate_cagr

Read-onlyIdempotent

Calculate the Compound Annual Growth Rate (CAGR): the smoothed annual growth rate that takes a beginning value to an ending value over a given number of years. Formula: CAGR = (End Value / Begin Value)^(1/n) - 1. WHEN TO USE: Use to state multi-year growth as a single comparable annualised rate (revenue growth, asset growth, fund performance) — the standard "growth per year" figure. WHEN NOT TO USE: Do NOT use when the beginning value is zero or negative (undefined), or when you need period-by-period volatility rather than a smoothed rate. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { cagr: decimal (e.g. 0.201 = 20.1%), cagr_pct: number (e.g. 20.1), inputs }. PARAMETERS: begin_value (required): Value at the START of the period, e.g. 100000. Must be > 0. end_value (required): Value at the END of the period, e.g. 250000. Must be > 0. periods (required): Number of years between the two values, e.g. 5. Must be > 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodsYesNumber of years between the two values, e.g. 5. Must be > 0.
end_valueYesValue at the END of the period, e.g. 250000. Must be > 0.
begin_valueYesValue at the START of the period, e.g. 100000. Must be > 0.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description goes beyond these by adding a dedicated BEHAVIOUR section: pure deterministic calculation, no side effects, no network/storage access, identical inputs produce identical outputs, and explicit error handling for division by zero or non-finite inputs. This is rich, non-redundant behavioral context.

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 labeled sections (formula, when to use, when not to use, behaviour, returns, parameters), making it scannable and front-loaded with the core purpose. It is somewhat long due to repeating parameter details already present in the schema, but every section otherwise serves a distinct purpose.

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?

No output schema exists, but the description specifies the exact JSON return shape with examples, the formula, input constraints, error behavior, and ideal use cases. An agent has everything needed to invoke the tool correctly and interpret the result accurately.

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 descriptions and exclusiveMinimum for all three parameters. The description's PARAMETERS section largely repeats the schema text, adding no new semantic meaning beyond the structured definitions. Baseline 3 applies because the schema carries the parameter documentation burden.

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?

States a specific verb and resource ('Calculate the Compound Annual Growth Rate'), includes the formula, and defines the metric as a smoothed annual growth rate. This clearly distinguishes it from sibling financial calculators such as calculate_future_value or calculate_present_value, making selection unambiguous.

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?

Provides explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections with concrete examples (revenue growth, asset growth, fund performance) and clear exclusions (zero/negative beginning value, need for period-by-period volatility). This gives an agent direct decision criteria for when to invoke this tool versus not.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a mathematically distinct calculation (single-sum PV/FV, annuity, perpetuity, payback, fund multiples). The descriptions include explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections with cross-references, making it unambiguous which tool applies to which scenario.

Naming Consistency5/5

All tool names follow the exact pattern verb_noun with the consistent 'calculate_' prefix (e.g., calculate_present_value, calculate_payback_period). The naming convention is uniform across the entire set.

Tool Count5/5

11 tools is well within the ideal range for a focused domain. Each tool addresses a distinct calculation relevant to time-value-of-money and fund performance, with no redundant entries.

Completeness3/5

The set covers many core calculations but is missing NPV and IRR, which are explicitly referenced as the recommended tools in several descriptions (e.g., payback periods, TVPI). This creates a notable gap that could lead agents to follow cross-references to non-existent tools.

Resources