Compute Systematic Investment Plan (SIP) mutual fund maturity with optional annual step-up percentage.
| Name | Required | Description | Default |
|---|---|---|---|
| tenureYears | No | Investment duration in years | |
| stepUpPercent | No | Annual step-up percentage (e.g. 10 for 10% annual increase) | |
| annualReturnRate | No | Expected annual return rate in % | |
| monthlyInvestment | Yes | Monthly SIP amount |
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 provided, the description carries the full burden of behavioral disclosure. It clearly states the core computation and the optional step-up feature, but it does not describe key behavioral details such as compounding frequency, how the step-up is applied over time, or what exact value is returned.
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, efficient sentence that front-loads the verb and resource, expands the acronym, and includes the key optional behavior. There is no filler or redundancy, making it highly scannable for an agent.
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 schema fully documents the parameters, and the description states the output concept ('maturity') and the step-up feature. However, since there is no output schema, the description could be more explicit about the exact return value and the assumptions behind the calculation, such as annual compounding or how the step-up changes the monthly investment.
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 four parameters with defaults and examples. The tool description adds only a mention of the optional annual step-up percentage, which reinforces stepUpPercent but does not materially add meaning beyond the schema.
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 ('Compute'), a specific resource ('Systematic Investment Plan (SIP) mutual fund maturity'), and adds a distinguishing feature ('optional annual step-up percentage'). This makes the tool's purpose immediately identifiable and separates it from sibling calculators like compound_wealth or home_loan_emi.
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 when the tool should be used: whenever a SIP maturity calculation is needed. However, it provides no explicit guidance about when not to use it or how it compares to sibling tools such as compound_wealth or home_loan_emi, leaving the agent to infer the appropriate 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.
