Calculate Net Present Value (NPV) and Internal Rate of Return (IRR) via iterative Newton-Raphson polynomial convergence for capital budgeting, investment appraisal, and payback periods.
| Name | Required | Description | Default |
|---|---|---|---|
| cashflows | Yes | Series of sequential cash inflows ($) | |
| initialInvestment | Yes | Initial capital outlay / outflow at period 0 ($) | |
| discountRatePercent | No | Annual hurdle / discount rate % |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose that the computation uses 'iterative Newton-Raphson polynomial convergence', which usefully signals an approximate numerical method. However, it does not mention potential convergence failures, multiple IRR scenarios, or how outputs are returned, so behavioral transparency is adequate but incomplete.
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 sentence that front-loads the core purpose before adding method and context details. It is reasonably concise, though the phrase 'iterative Newton-Raphson polynomial convergence' is somewhat technical and could be trimmed without losing selection value.
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?
There is no output schema and no annotations, so the description should ideally clarify what the tool returns and any edge-case behavior. It explains the calculation approach and use cases, but does not specify whether the tool returns both NPV and IRR, in what form, or how non-convergence is handled. This is a moderate gap for an unannotated tool.
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 100%, so the input schema already documents all three parameters clearly. The description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.
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 uses a specific verb and resource: 'Calculate Net Present Value (NPV) and Internal Rate of Return (IRR)'. It clearly identifies the mathematical method and the intended application domains, and the name plus description distinguish it from financial siblings like black_scholes or breakeven_margin.
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 phrases 'for capital budgeting, investment appraisal, and payback periods' give clear intended-use context for an agent deciding between this tool and other financial calculators. It does not name alternative tools or explicit exclusion criteria, but the usage domain is specific enough to guide selection.
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 are clearly separated by domain and target calculation, such as rocket_deltav versus projectile_motion or black_scholes versus compound_wealth. A few pairs like home_loan_emi/mortgage_piti and contractor_parity/billable_floor could be initially confused, but the descriptions resolve the intended use cases.
All tool names are lowercase snake_case and generally follow a topic-plus-suffix pattern, which is readable and consistent. The pattern is not a strict verb_noun convention, and acronym-heavy names like feie_nomad_tracker, scorp_optimizer, and casio_991_solve introduce stylistic variance.
At exactly 25 tools, this is at the heavy but still usable end of the scale. The broad spread across tax, finance, engineering, physics, math, and cloud cost makes the server feel more like several domain calculators merged into one service.
Each tool is a self-contained calculation with no missing follow-up operations, so there are no obvious dead ends for the workflows it targets. The main gaps are minor adjacent calculators—such as NPV, depreciation, or broader statistical inference—that agents could work around or obtain elsewhere.
