calc_irr
COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| guess | No | ||
| cashflows | Yes |
COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.
| Name | Required | Description | Default |
|---|---|---|---|
| guess | No | ||
| cashflows | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the numerical method (Newton + bisection) and the sign-change requirement, which gives some insight into behavior. However, it does not describe what happens when the sign-change condition is not met, how multiple IRRs are handled, or how the guess parameter affects convergence.
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 a single, front-loaded sentence that conveys the core purpose and a key constraint without any fluff. Every word contributes meaning, making it highly concise and easy to parse.
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?
The tool has two parameters and no output schema, so the description must explain parameters and return values. It covers the main calculation and the sign-change condition, but the 'guess' parameter is entirely undocumented, and the return format (a single number vs. an object) is not stated. For a simple financial calculation, this is adequate but has clear gaps.
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 description coverage is 0%, so the description must compensate for parameter documentation. It only references 'cashflows' indirectly via the sign-change requirement and does not explain the expected ordering (e.g., initial investment as negative) or the purpose of the 'guess' parameter at all. This leaves significant ambiguity for a tool with only two parameters.
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 the internal rate of return, defined as the per-period rate where NPV=0. This directly distinguishes it from the sibling calc_npv tool by describing the relationship to NPV. The verb 'COMPUTE' is generic, but the resource and mathematical definition are explicit.
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 provides a clear usage precondition: 'Requires a sign change in cashflows.' This tells the agent when it is appropriate to use the tool. However, it does not explicitly mention alternatives (e.g., calc_npv) or exclusions beyond the sign-change requirement, so it falls just short of a 5.
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.