COMPUTE. Compound-interest/growth math. operation: future_value | present_value | cagr. rate is annual decimal; compounding: daily|weekly|monthly|quarterly|semiannually|annually|continuous.
| Name | Required | Description | Default |
|---|---|---|---|
| rate | No | ||
| years | No | ||
| currency | No | ||
| end_value | No | ||
| operation | Yes | ||
| begin_value | No | ||
| compounding | No | ||
| future_value | No | ||
| present_value | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description adds useful behavioral context by specifying that rate is an annual decimal and by enumerating valid compounding frequencies. However, it does not disclose parameter relationships, defaults, edge cases, or return semantics, so significant behavioral ambiguity remains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief, front-loaded, and includes key value domains without wasted words. It is appropriately compact, though the brevity also contributes to under-specification in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, no output schema, and no annotations, the description is too sparse to support confident invocation. It covers operation types and compounding frequencies but omits required-parameter relationships, units for years/currency, and expected result shapes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description only clarifies rate's format and compounding values, leaving most of the 9 parameters unexplained. It does not map parameters to specific operations, so a user cannot determine which inputs are required for future_value versus cagr.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it computes compound-interest/growth math and enumerates the supported operations (future_value, present_value, cagr), which makes the purpose specific and generally distinguishable from sibling financial calculators like calc_irr and calc_npv. However, it does not explicitly contrast itself with any sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used for compound-interest or growth calculations and lists operation modes, but it gives no explicit when-to-use guidance or alternatives. Users are left to infer when to choose this over calc_irr, calc_npv, or calc_depreciation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear, distinct purposes across three namespaces (calc_, decide_, sim_) plus composites. Some conceptual overlap exists (e.g., decide_sensitivity vs. sim_sensitivity, decide_score vs. decide), but descriptions clarify the boundaries well.
Names follow a consistent snake_case convention with a namespace prefix (calc_, decide_, sim_) and a descriptive verb_noun structure. Even composite tools and utilities like health_check and list_capabilities fit the pattern.
24 tools is on the heavier side, but it's justified for a meta-server exposing three distinct engines plus cross-domain composites. The count is appropriately scoped for the breadth of capabilities advertised.
The set covers all core domains with discovery (list_capabilities, *_list_*), health_check, and composite tools linking simulation to decision and valuation. Minor gaps include lack of a template management tool, but sim_run accepts free-form models, mitigating this.