Compute reducing balance monthly loan EMI and total interest with amortization schedule.
| Name | Required | Description | Default |
|---|---|---|---|
| principal | Yes | Loan amount | |
| tenureYears | No | Loan term in years | |
| interestRatePercent | Yes | Annual interest 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?
No annotations are provided, so the description carries the full behavioral disclosure burden. It clearly states it computes EMI, total interest, and amortization schedule, which implies a read-only calculation with no side effects. However, it does not disclose assumptions like monthly compounding frequency, rounding behavior, or handling of zero/negative values. For a pure computation tool, this is adequate but not exhaustive.
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, concise sentence that front-loads the primary action (Compute) and immediately states the outputs (EMI, total interest, amortization schedule). Every word earns its place; there is no fluff or redundancy.
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 tool with no output schema, the description adequately communicates the return values: EMI, total interest, and amortization schedule. However, it could specify the exact format (e.g., monthly schedule rows, currency, rounding) and any key assumptions (e.g., annual compounding). Despite these minor gaps, it is sufficiently complete for an agent to use the tool correctly for typical home loan calculations.
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 all parameters (principal, tenureYears, interestRatePercent) are documented in the schema. The description adds context about the reducing balance method but does not provide additional parameter-level details beyond the schema. Baseline 3 is appropriate because the schema handles the parameter documentation.
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 the verb 'Compute' and the specific resource: reducing balance monthly loan EMI, total interest, and amortization schedule. It distinguishes this tool from siblings like mortgage_piti by specifying the reducing balance method and home loan context. An agent can immediately identify the tool's purpose without ambiguity.
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 usage for home loan EMI calculations but does not explicitly contrast with alternatives like mortgage_piti or sip_investment. There are no exclusions or conditions for when to choose this tool over siblings. Usage is inferred from the name and description, but no explicit routing guidance is provided.
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.
