Simulate exponential compounding wealth for 401(k), Roth IRA, UK ISA, or European ETF savings plans (Sparplan).
| Name | Required | Description | Default |
|---|---|---|---|
| principal | No | Initial principal deposit | |
| tenureYears | Yes | Duration in years | |
| monthlyDeposit | No | Monthly recurring contribution | |
| annualRatePercent | Yes | Expected annual return in % | |
| compoundFrequency | No | Compounding frequency per year (12 = monthly) |
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 must carry the behavioral burden. 'Simulate' signals a calculation rather than a mutation, but the description does not state whether the result is a final balance, a schedule, or a projection, nor which assumptions (taxes, fees, inflation) are ignored.
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?
A single front-loaded sentence; the key verb and domain appear first, followed by the specific eligible plans. There is no filler, repetition, or unnecessary detail.
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?
For a simple calculator the description is nearly sufficient, but with no output schema and no annotations, it leaves the return format and modeling assumptions unstated. The schema covers the inputs well, so the main missing piece is what the tool returns when invoked.
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?
The input schema already covers all five parameters with descriptions and defaults, so the baseline is 3. The description adds account-scope context but no extra information about how annualRatePercent, monthlyDeposit, or compoundFrequency interact.
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 the specific verb 'Simulate' with a concrete object ('exponential compounding wealth') and narrows the domain to named account types (401(k), Roth IRA, UK ISA, European ETF savings plans). This makes it plainly distinct from the sibling calculators even though it does not name them.
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 account-type list gives a clear trigger context: retirement or tax-advantaged savings plans in the US, UK, or EU. It stops short of explicit alternatives or exclusion statements, e.g., it never mentions that sip_investment may be a better fit for Indian SIP-style contributions.
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.
